Question answering is a very popular task in Natural language processing but question generation is novel and hasn’t been explored much yet.
If you want to try a live demo of question generation in action, please visit https://questgen.ai/
Question generation has a lot of use cases with the most prominent…
You might have seen the traditional word2vec or Glove word embeddings examples that show King -Man+Woman = Queen. Here Queen will be returned from the word embedding algorithm given the words King, Man, and Woman. Today we will see how we can use this structure to solve a real-world problem.
Using GPU within a docker container isn’t straightforward. There shouldn't be any mismatch between CUDA and CuDNN drivers on both the container and host machine to enable seamless communication.
The input to our program will be any English sentence -
Four private astronauts launched to orbit by Elon Musk’s SpaceX returned to Earth Saturday evening, splashing down into the ocean off the east coast of Florida after a three-day mission.
The output will be paraphrased version of the same…
In semantic search, we search a database of documents for a given user query and get back a set of relevant documents.
Whether we are building a recommendation system or question answering system often times we get back duplicate search results that are not exactly identical to be called duplicates…
This year I launched my first ever online course on Udemy titled “Question generation using Natural Language processing” and made $3333 dollars in 5 months with 0 marketing spend and 100% paid enrollments.
Distractors are the wrong answers in a multiple-choice question.
For example, if a given multiple choice question has the game Cricket as the correct answer then we need to generate wrong choices (distractors) like Football, Golf, Ultimate Frisbee, etc.
I’m sure most of you have heard about OpenAI’s GPT-3 and its insane text generation capabilities learning from only a few examples.
The concept of feeding a model with very little training data and making it learn to do a novel task is called Few-shot learning.
A website GPT-3 examples…
Imagine that you are a writer and you are searching for the best image that goes with your blog or book. You have a search phrase in mind like “Tiger playing in the snow”. …