For question generation the answer spans are highlighted within the text with special highlight tokens (<hl>) and prefixed with 'generate question: '. For QA the input is processed like this question: question_text context: context_text </s>

You can play with the model using the inference API. Here's how you can use it

For QG

generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>

For QA

question: What is 42 context: 42 is the answer to life, the universe and everything. </s>

For more deatils see this repo.

Model in action 🚀

You'll need to clone the repo.

Open In Colab

python3

from pipelines import pipeline
nlp = pipeline("multitask-qa-qg")

# to generate questions simply pass the text
nlp("42 is the answer to life, the universe and everything.")
=> [{'answer': '42', 'question': 'What is the answer to life, the universe and everything?'}]

# for qa pass a dict with "question" and "context"
nlp({
    "question": "What is 42 ?",
    "context": "42 is the answer to life, the universe and everything."
})
=> 'the answer to life, the universe and everything'

valhalla/t5-small-qa-qg-hl

T5 for multi-task QA and QG

Model in action 🚀

valhalla/
t5-small-qa-qg-hl