NLP Case Studies: Article Text Comprehension & Question Answering
Natural Language Processing | Intermediate
- 7 videos | 28m 36s
- Includes Assessment
- Earns a Badge
Most current question answering datasets will frame the task as reading comprehension, where the question is about a paragraph or document and the answer often is a span in the document. Some specific tasks of reading comprehension include multi-modal machine reading comprehension and textual machine reading comprehension, among others. This course focuses on the architecture of the Q&A pipeline. First, install the Transformers library and import a text comprehension model to create your Q&S pipeline. Then, use Gradio to develop a user interface for answering questions about a given article. Upon completion, you'll be able to develop an application that can answer questions asked by a user about a given article.
WHAT YOU WILL LEARN
Discover the key concepts covered in this courseDescribe the steps and overall architecture of the q&a pipeline and text comprehension applicationInstall the pytorch and transformers librariesImport a text comprehension model and create a q&a pipeline
Recall examples of how to use the text comprehension modelCreate an app for reading comprehension that answers questions from an articleSummarize the key concepts covered in this course
IN THIS COURSE
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