Natural Language Processing with Hugging Face Transformers
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Learn Natural Language Processing (NLP) with Hugging Face Transformers. Master techniques like text classification, translation, and sentiment analysis using state-of-the-art models.
At a Glance
This Guided Project will walk you through some of the applications of Hugging Face Transformers in Natural Language Processing (NLP). Hugging Face Transformers provide pre-trained models for a variety of applications in NLP and Computer Vision. For example, these models are widely used in near real-time translation tasks, opening communication spaces to language-diverse and hearing-impaired audiences. In this project, you will learn and practice applying these models to do text summarization, sentiment classification, translation, generate new text, and extract information from text.
Why you should do this Guided Project
A Look at the Project Ahead
– perform text classification, such as sentiment analysis
– perform topic classification
– generate some text
– perform token classification, such as Name Entity Recognition (NER)
– extract some information by doing question answering analysis
– do text summarization
– translate text from one language to another
What You’ll Need
Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to the Cloud IDE and Python runtimes that we offer as part of the IBM Skills Network Labs environment. Remember that the IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save them the hassle of setting everything up. Also note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.
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