README.md

metadata

datasets:
  - squad
tags:
  - question-generation
  - distilt5
  - distilt5-qg
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

DistilT5 for question-generation

This is distilled version of t5-base-qa-qg-hl model trained for question answering and answer aware question generation tasks.

The model is distilled using the No Teacher Distillation method proposed by Huggingface, here.

We just copy alternating layers from t5-base-qa-qg-hl and finetune more on the same data. Following table lists other distilled models and their metrics.

Name	BLEU-4	METEOR	ROUGE-L	QA-EM	QA-F1
distilt5-qg-hl-6-4	18.4141	24.8417	40.3435	-	-
distilt5-qa-qg-hl-6-4	18.6493	24.9685	40.5605	76.13	84.659
distilt5-qg-hl-12-6	20.5275	26.5010	43.2676	-	-
distilt5-qa-qg-hl-12-6	20.6109	26.4533	43.0895	81.61	89.831

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.

For QA

question: What is 42 context: 42 is the answer to life, the universe and everything.

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", model="valhalla/distilt5-qa-qg-hl-12-6")

# 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'