Practice Masked Prediction Models - 11.2.3.2 | 11. Representation Learning & Structured Prediction | Advance Machine Learning
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skillsβ€”perfect for learners of all ages.

games

11.2.3.2 - Masked Prediction Models

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does the '[MASK]' token signify in masked prediction models?

πŸ’‘ Hint: Think about what is missing in a sentence.

Question 2

Easy

Name one application of masked prediction models.

πŸ’‘ Hint: Consider how models understand user emotions.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the main function of the [MASK] token in masked prediction?

  • [MASK] serves as a substitute for the actual token.
  • [MASK] is used for formatting the text.
  • [MASK] indicates an error in input.

πŸ’‘ Hint: Think about what you need the model to guess.

Question 2

True or False: Masked prediction models can only predict tokens that appear in the training data.

  • True
  • False

πŸ’‘ Hint: Consider how models learn from examples.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a simple masked prediction model using a small dataset of sentences. What metrics would you use to evaluate its performance?

πŸ’‘ Hint: Consider the standard metrics used in machine learning for measuring effectiveness.

Question 2

Consider potential ethical implications of utilizing masked prediction models in real-world applications like facial recognition or hiring algorithms. Discuss how biases might arise.

πŸ’‘ Hint: Reflect on the data's source and the context it’s applied.

Challenge and get performance evaluation