Offered By: IBMSkillsNetwork
Text Sentiment Analysis using Caikit and Hugging Face
In this guided project you will build a Python application that uses Caikit run-time and API to query the Hugging Face model for sentiment analysis on text strings.
Continue readingGuided Project
Artificial Intelligence
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At a Glance
In this guided project you will build a Python application that uses Caikit run-time and API to query the Hugging Face model for sentiment analysis on text strings.
In this project you will learn to perform Text Sentiment Analysis using Caikit and Hugging Face. Discover the power of leveraging a Hugging Face sentiment analysis model and calling it through APIs from a Python-based Caikit run-time. By following this tutorial, you will gain the skills to use Caikit as an abstraction layer that enables you to utilize and manage various kinds of AI models using a consistent interface.
Caikit is an advanced AI toolkit designed to simplify the process of working with models through user-friendly APIs. With Caikit, you gain access to a powerful set of tools that enable seamless model integration across diverse data domains and tasks.Â
Hugging Face provides an extensive collection of pre-trained models, allowing developers and researchers to leverage cutting-edge NLP and Generative AI capabilities with ease. The platform also offers user-friendly APIs, empowering users to integrate these models seamlessly into their own applications and projects.
Hugging Face provides an extensive collection of pre-trained models, allowing developers and researchers to leverage cutting-edge NLP and Generative AI capabilities with ease. The platform also offers user-friendly APIs, empowering users to integrate these models seamlessly into their own applications and projects.
A Look at the Project Ahead
This guided project covers various aspects, such as setting up the development environment and Caikit runtime, interfacing with the the Hugging Face model, and applying it to analyze text sentiment. Throughout the project, you'll find step-by-step instructions, allowing you to write the code and execute commands hands-on in a dedicated environment. By the end, you'll have a clear understanding of how to utilize the Caikit run-time and use it to communicate with a sentiment analysis model on Hugging Face using APIs from a Python application.
Key learning objectives of this project include:
- Installing the required Python libraries and Caikit run-time.
- Exploring Caikit and understanding its capabilities in the context of model management.
- Leveraging a Hugging Face model for sentiment classification.
- Implementing API calls to interact with the sentiment analysis model seamlessly.
Whether you're a beginner or an experienced Python developer, this tutorial caters to all skill levels. Take advantage of the simplicity and versatility offered by Caikit's developer-friendly interface to access models using APIs, empowering you to harness the full potential of AI without unnecessary complexity.
What You'll Need
The Guided Project environment includes a Cloud IDE and a run-time environment that you can access from your web browser. The virtual hands-on environment provides you with all the software and tools you need to complete the project, install the necessary Python packages, and execute the step-by-step instructions, saving you the hassle of setting everything up on your own system.This platform works best with current versions of Chrome, Edge, Firefox or Safari.