Generate Boolean (Yes/No) Questions From Any Content
Question generation algorithm trained on the BoolQ dataset using T5 text-to-text transformer model.
question-generation transformers huggingface t5 pytorch natural-language-processing article code tutorial research

Generate boolean (yes/no) questions from any content using T5 text-to-text transformer model and BoolQ dataset

Pre-trained model and training script are provided

Input

The input to our program will be any content/paragraph -

Months earlier, Coca-Cola had begun “Project Kansas.” It sounds like a nuclear experiment but it was just a testing project for the new flavor. In individual surveys, they’d found that more than 75% of respondents loved the taste, 15% were indifferent, and 10% had a strong aversion to the taste to the point that they were angry.

Ouput

The output will be boolean (yes/no) questions generated from the above input.

Boolean (yes/no) questions generated from the T5 Model :

1: Does coca cola have a kansas flavor?

2: Is project kansas a new coca cola flavor?

3: Is project kansas the same as coca cola?

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Authors original post
Lead Data Scientist at Right-Hand Cybersecurity || BITS Pilani || Ex Co-Founder & CTO of a funded AI startup
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