Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- squad
+language:
+- en
+library_name: transformers
+pipeline_tag: text2text-generation
+---
+# t5-large fine-tuned to SQuAD for Generating Question+Answer
+- Input: `context` (e.g. news article)
+- Output: `question <sep> answer`
+
+The answers in the training data (SQuAD) are highly extractive; therefore, this model will generate **extractive** answers. If you would like to have **abstractive** questions/answers, you can use our model trained on the RACE dataset: https://huggingface.co/potsawee/t5-large-generation-race-QuestionAnswer. 
+
+## Model Details
+
+t5-large model is fine-tuned to the SQuAD 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. You can also set ```do_sample=True``` in ```generate()``` to obtain different question-answer pairs.
+
+```python
+>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+>>> tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer")
+>>> model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer")
+
+>>> context = r"""Chelsea's mini-revival continued with a third victory in a row as they consigned struggling Leicester City to a fifth consecutive defeat.
+Buoyed by their Champions League win over Borussia Dortmund, Chelsea started brightly and Ben Chilwell volleyed in from a tight angle against his old club.
+Chelsea's Joao Felix and Leicester's Kiernan Dewsbury-Hall hit the woodwork in the space of two minutes, then Felix had a goal ruled out by the video assistant referee for offside.
+Patson Daka rifled home an excellent equaliser after Ricardo Pereira won the ball off the dawdling Felix outside the box.
+But Kai Havertz pounced six minutes into first-half injury time with an excellent dinked finish from Enzo Fernandez's clever aerial ball.
+Mykhailo Mudryk thought he had his first goal for the Blues after the break but his effort was disallowed for offside.
+Mateo Kovacic sealed the win as he volleyed in from Mudryk's header.
+The sliding Foxes, who ended with 10 men following Wout Faes' late dismissal for a second booking, now just sit one point outside the relegation zone.
+""".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:  Who scored the winner for Chelsea?
+>>> print("answer:", answer)
+answer:  Mateo Kovacic
+
+```
+
+## Generating Distractors (other options in a multiple-choice setup)
+
+```Context ---> Question + (A) Answer (B) Distractor1 (C) Distractor2 (D) Distractor3```
+
+Please refer to our distractor generation model, e.g. https://huggingface.co/potsawee/t5-large-generation-race-Distractor
+
+## 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}
+}
+```