Mixqg Base

🚀 MixQG (base-sized model)

MixQG is a novel question generation model. It's pre - trained on a set of QA datasets with diverse answer types. This model was presented in the paper MixQG: Neural Question Generation with Mixed Answer Types, and the corresponding code is available in this repository.

🚀 Quick Start

How to use

💻 Usage Examples

Basic Usage

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."
ANSWER = "Robert Boyle"

def format_inputs(context: str, answer: str):
    return f"{answer} \\n {context}"

text = format_inputs(CONTEXT, ANSWER)

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

Advanced Usage

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."
ANSWER = "Robert Boyle"

def format_inputs(context: str, answer: str):
    return f"{answer} \\n {context}"

text = format_inputs(CONTEXT, ANSWER)

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?"

📄 License

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}
}

📚 Documentation

Ethical Considerations

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes.

⚠️ Important Note

We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model.

💡 Usage Tip

We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high - risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.