BenchMark Quizzes by MDFT Pro

Build Custom Classification Model For Educational Content Types

MDFT Pro, a well-known training agency, has acquired a large digital library containing 10,000 PDF documents that include various types of educational content: technical textbooks, graphic novels used for creative writing courses, and educational magazines covering industry trends. Claire, the Content Management Director, needs to automatically categorize this diverse collection to improve searchability and organization for students and instructors.

The PDFs contain scanned books, comics, and magazines that need to be tagged appropriately so the learning management system can recommend appropriate materials based on course type and student preferences. The solution must accurately classify each document while requiring minimal manual development effort.

What type of model should Claire build using Language Studio to categorize these educational materials?

Choose the correct answer from the options below.

Explanations for each answer:

A multi-label image classification model is incorrect. Image classification is used for visual content analysis, but the documents are PDFs containing text content that needs text-based classification.
A multi-label classification project is correct. Multi-label classification in Language Studio can categorize text documents into predefined categories (book, comic, magazine) with minimal development effort.
A custom extraction model is incorrect. Extraction models are designed to pull specific information from documents, not to classify the overall document type or category.
A custom named entity recognition (NER) project is incorrect. NER identifies specific entities within text but doesn't classify the document type - it finds people, places, organizations, not content categories.

Learn more about custom text classification:

Custom Text Classification

Exam Preparation Quiz

Build Custom Classification Model For Educational Content Types