Implement Named Entity Recognition with BERT
In this course, you’ll learn about the state-of-the-art transformer technique called BERT, how to tag respective news domain entities to classify information, and how to get relevant insights about the geo-political news using deep learning library.
Classifying information into multiple domain entities is quite important for an enterprise to garner key insights. In this course, Implement Named Entity Recognition with BERT, you’ll gain the ability to tag referential entities based on domain text. First you’ll explore the key benefits of transformers. Next, you’ll discover the usage and advantages of named entity recognition. Finally, you’ll use PyTorch to tag entities based on domain data and BERT technique. When you’re finished with this course, you’ll have the skills and knowledge on how to implement named entity recognition with BERT and retrieve context about the text which leads to contextual tagging.
Author Name: Herta Nava
Author Description:
Herta is an independent Consultant working in software localization, web technologies, online marketing and course authoring. She is mostly dedicated to authoring courses with Pluralsight, but she also works as a localization consultant for SDL International and is part of the Google Linguistic and Localization Team, where she is assigned to localize Google products and software. Herta is an Apple Certified Support Professional (ASCP) and holds degrees and certifications in Web Programming… more
Table of Contents
- Course Overview
1min - Introducing Key Benefits of Transformers
4mins - Analyzing Key Differentiators of BERT
10mins - Leveraging Key Benefits of NER for Geopolitical Data
13mins - Implementing BERT Based NER Using PyTorch
8mins
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