Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- race
+language:
+- en
+library_name: transformers
+pipeline_tag: text2text-generation
+---
+# t5-large fine-tuned to RACE for Generating Question+Answer
+- Input: `context` (e.g. news article)
+- Output: `question <sep> answer`
+
+## Model Details
+
+t5-large model is fine-tuned to the RACE dataset where the input is the context/passage and the output is the question followed by the answer. This is the first component in the question generation pipeline (i.e. `g1`) in our [MQAG paper](https://arxiv.org/abs/2301.12307), 
+or please refer to the GitHub repo of this project: https://github.com/potsawee/mqag0.
+
+## How to Use the Model
+
+Use the code below to get started with the model. 
+
+```python
+>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+>>> tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-race-QuestionAnswer")
+>>> model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-race-QuestionAnswer")
+
+>>> context = r"""
+... World number one Novak Djokovic says he is hoping for a "positive decision" to allow him 
+... to play at Indian Wells and the Miami Open next month. The United States has extended 
+... its requirement for international visitors to be vaccinated against Covid-19. Proof of vaccination 
+... will be required to enter the country until at least 10 April, but the Serbian has previously 
+... said he is unvaccinated. The 35-year-old has applied for special permission to enter the country. 
+... Indian Wells and the Miami Open - two of the most prestigious tournaments on the tennis calendar 
+... outside the Grand Slams - start on 6 and 20 March respectively. Djokovic says he will return to 
+... the ATP tour in Dubai next week after claiming a record-extending 10th Australian Open title 
+... and a record-equalling 22nd Grand Slam men's title last month.""".replace("\n", "")
+
+>>> inputs = tokenizer(context, return_tensors="pt")
+>>> outputs = model.generate(**inputs, max_length=100)
+>>> question_answer = tokenizer.decode(outputs[0], skip_special_tokens=False)
+>>> question_answer = question_answer.replace(tokenizer.pad_token, "").replace(tokenizer.eos_token, "")
+>>> question, answer = question_answer.split(tokenizer.sep_token)
+
+>>> print("question:", question)
+question:  What is the best title for the passage?
+>>> print("answer:", answer)
+answer:  Djokovic's application for special permission to enter the United States
+
+```
+
+
+## Citation
+
+```bibtex
+@article{manakul2023mqag,
+  title={MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization},
+  author={Manakul, Potsawee and Liusie, Adian and Gales, Mark JF},
+  journal={arXiv preprint arXiv:2301.12307},
+  year={2023}
+}
+```