Create README.md

README.md

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+---
+language: en
+widget:
+- text: Robert Boyle \\n In the late 17th century, Robert Boyle proved that air is necessary for combustion.
+---
+
+# MixQG (large-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](https://arxiv.org/abs/2110.08175) and the associated code is released in [this](https://github.com/salesforce/QGen) repository.
+
+### How to use
+Using Huggingface pipeline abstraction:
+```
+from transformers import pipeline
+
+nlp = pipeline("text2text-generation", model='Salesforce/mixqg-large', tokenizer='Salesforce/mixqg-large')
+    
+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?'}]
+```
+Using the pre-trained model directly:
+```
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained('Salesforce/mixqg-large')
+model = AutoModelForSeq2SeqLM.from_pretrained('Salesforce/mixqg-large')
+
+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?"
+```
+
+### 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}
+}
+```