metadata
datasets:
- squad
tags:
- question-generation
widget:
- text: >-
generate question: <hl> 42 <hl> is the answer to life, the universe and
everything. </s>
- text: >-
question: What is 42 context: 42 is the answer to life, the universe and
everything. </s>
license: mit
T5 for multi-task QA and QG
This is multi-task t5-small model trained for question answering and answer aware question generation tasks.
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.
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'