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
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Discover the key concepts covered in this course
Describe the steps and overall architecture of the q&a pipeline and text comprehension application
Install the pytorch and transformers libraries
Import a text comprehension model and create a q&a pipeline -
Recall examples of how to use the text comprehension model
Create an app for reading comprehension that answers questions from an article
Summarize the key concepts covered in this course
IN THIS COURSE
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1m 16s
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2m 9s
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3. Installing PyTorch and Transformers Libraries4m 27s
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4. Importing a Text Comprehension Model8m 16s
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5. Using a Text Comprehension Model5m 28s
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6. Developing a Text Comprehension App Using Gradio5m 50s
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7. Course Summary1m 10s
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