Offered By: IBM
From Chaos to Order: Automate Documents Categorization by AI
Construct a news classifier for a content search engine using TorchText, while gaining a deep understanding of NLP fundamentals, including embeddings and tokenization. The headlines will be categorized into World, Sports, Business, and Science/Tech, which can be adapted to your specific use case.
Continue readingGuided Project
Artificial Intelligence
99 Enrolled5.0
At a Glance
Construct a news classifier for a content search engine using TorchText, while gaining a deep understanding of NLP fundamentals, including embeddings and tokenization. The headlines will be categorized into World, Sports, Business, and Science/Tech, which can be adapted to your specific use case.
Imagine working at a prestigious newspaper or magazine company that boasts an extensive archive of documents dating back through the annals of time. Within this treasure trove of information, a monumental task awaits: organizing these historical documents into their relevant topic sections, distinguishing between subjects like sports and science or other categories pertinent to your use case. The implementation of an automated machine learning system greatly enhances efficiency in this process. Such a system, equipped with advanced natural language processing and machine learning capabilities, could meticulously sift through the vast archives, categorizing articles into their respective topics with remarkable precision. In this project, you will embark on the exciting endeavor of classifying news articles for a content search engine. The ultimate objective is to construct a model capable of automatically categorizing news articles into distinct topics or classes, thereby empowering the search engine to efficiently deliver relevant content to users.
Natural Language Processing (NLP) plays a crucial role in understanding the intricate workings of Large Language Models (LLMs). In this project, we will thoroughly explore the fundamentals of NLP, covering everything from tokenization to embedding, to gain a deeper understanding of how these models decode and utilize language. By learning these fundamental concepts, you will gain a new perspective on the high-end capabilities of NLPs i.e. LLMs. These powerful models have the remarkable ability to make sense of words and sentences, comprehending the nuances of language comprehension. The project will follow a structured approach, starting with hands-on practice of the basics and gradually progressing to the implementation of your very own news classifier. Through this project, you will develop practical skills and insights into building text classification models for real-world applications.
Natural Language Processing (NLP) plays a crucial role in understanding the intricate workings of Large Language Models (LLMs). In this project, we will thoroughly explore the fundamentals of NLP, covering everything from tokenization to embedding, to gain a deeper understanding of how these models decode and utilize language. By learning these fundamental concepts, you will gain a new perspective on the high-end capabilities of NLPs i.e. LLMs. These powerful models have the remarkable ability to make sense of words and sentences, comprehending the nuances of language comprehension. The project will follow a structured approach, starting with hands-on practice of the basics and gradually progressing to the implementation of your very own news classifier. Through this project, you will develop practical skills and insights into building text classification models for real-world applications.
A Look at the Project Ahead
Once you start the project, you'll be learning about:
- Work with datasets and understand tokenizer, embedding bag technique and vocabulary.
- Explore embeddings in PyTorch and understand token indices.
- Perform text classification using data loader and apply it on a neural network model.
- Train the text classification model on a news dataset.
What You'll Need
Prior to starting this guided project, learners should have a basic understanding of Python programming. The IBM Skills Network Labs environment comes pre-installed with necessary tools, eliminating the need for complex setup, making it accessible and convenient for all learners.