# MixQG (base-sized model)

MixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It was introduced in the paper MixQG: Neural Question Generation with Mixed Answer Types and the associated code is released in this repository.

### How to use

Using Huggingface pipeline abstraction:

from transformers import pipeline

nlp = pipeline("text2text-generation", model='Salesforce/mixqg-base', tokenizer='Salesforce/mixqg-base')

CONTEXT = "In the late 17th century, Robert Boyle proved that air is necessary for combustion."

nlp(text)
# should output [{'generated_text': 'Who proved that air is necessary for combustion?'}]


Using the pre-trained model directly:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained('Salesforce/mixqg-base')
model = AutoModelForSeq2SeqLM.from_pretrained('Salesforce/mixqg-base')

CONTEXT = "In the late 17th century, Robert Boyle proved that air is necessary for combustion."

input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=32, num_beams=4)
output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
print(output)
# should output "Who proved that air is necessary for combustion?"


### Citation

@misc{murakhovska2021mixqg,
title={MixQG: Neural Question Generation with Mixed Answer Types},
author={Lidiya Murakhovs'ka and Chien-Sheng Wu and Tong Niu and Wenhao Liu and Caiming Xiong},
year={2021},
eprint={2110.08175},
archivePrefix={arXiv},
primaryClass={cs.CL}
}