Natural Language Processing with Hugging Face Transformers
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.
4.6 (647 Reviews)
Language
- English
Topic
- Artificial Intelligence
Enrollment Count
- 2.32K
Skills You Will Learn
- Sentiment Analysis, Natural Language Processing, Python, Embeddable AI, LLM, PyTorch
Offered By
- IBM
Estimated Effort
- 30 min
Platform
- SkillsNetwork
Last Update
- December 22, 2024
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.
Language
- English
Topic
- Artificial Intelligence
Enrollment Count
- 2.32K
Skills You Will Learn
- Sentiment Analysis, Natural Language Processing, Python, Embeddable AI, LLM, PyTorch
Offered By
- IBM
Estimated Effort
- 30 min
Platform
- SkillsNetwork
Last Update
- December 22, 2024
Instructors
Svitlana Kramar
Data Scientist
I’m a passionate data science educator whose goal is to learn by teaching innovative data science tools that can improve our day-to-day tasks and our quality of life. My interests are in Natural Language Processing: text classification, summarization, and generation. Research can take a long time because there are a lot of resources and new opinions posted every day. Having tools to summarize and extract the information can save a lot of time. I hope we can all learn, approve, and apply the data science tools to cut down on the repetitive and tedious tasks, to make more informed decisions in life, to differentiate fake from real, and to open communication spaces to language-diverse or hearing-impaired audiences. The applications are limitless! My personality: I am a foodie and I love cooking and learning different cuisines. I also love travelling and connecting with people by learning a little bit of their language, about their food and music. I hold Data Science and Analytics master’s degree, specializing in Machine Learning, from University of Calgary.
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Sheng-Kai Chen
Data Scientist
Sheng-Kai Chen is a graduate student at the University of Toronto, concentrating on Information Systems & Design. Having several experiences analyzing data for retail stores and designing small software for small businesses. Sheng-Kai was inspired to shift toward answering new challenges with machine learning and new technics.
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