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--- |
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license: apache-2.0 |
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datasets: |
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- race |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text2text-generation |
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--- |
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# t5-large fine-tuned to RACE for Generating Question+Answer |
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- Input: `context` (e.g. news article) |
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- Output: `question <sep> answer` |
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## Model Details |
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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), |
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or please refer to the GitHub repo of this project: https://github.com/potsawee/mqag0. |
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## How to Use the Model |
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Use the code below to get started with the model. |
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```python |
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>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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>>> tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-race-QuestionAnswer") |
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>>> model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-race-QuestionAnswer") |
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>>> context = r""" |
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... World number one Novak Djokovic says he is hoping for a "positive decision" to allow him |
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... to play at Indian Wells and the Miami Open next month. The United States has extended |
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... its requirement for international visitors to be vaccinated against Covid-19. Proof of vaccination |
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... will be required to enter the country until at least 10 April, but the Serbian has previously |
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... said he is unvaccinated. The 35-year-old has applied for special permission to enter the country. |
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... Indian Wells and the Miami Open - two of the most prestigious tournaments on the tennis calendar |
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... outside the Grand Slams - start on 6 and 20 March respectively. Djokovic says he will return to |
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... the ATP tour in Dubai next week after claiming a record-extending 10th Australian Open title |
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... and a record-equalling 22nd Grand Slam men's title last month.""".replace("\n", "") |
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>>> inputs = tokenizer(context, return_tensors="pt") |
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>>> outputs = model.generate(**inputs, max_length=100) |
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>>> question_answer = tokenizer.decode(outputs[0], skip_special_tokens=False) |
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>>> question_answer = question_answer.replace(tokenizer.pad_token, "").replace(tokenizer.eos_token, "") |
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>>> question, answer = question_answer.split(tokenizer.sep_token) |
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>>> print("question:", question) |
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question: What is the best title for the passage? |
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>>> print("answer:", answer) |
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answer: Djokovic's application for special permission to enter the United States |
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``` |
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## Generating Distractors (other options in a multiple-choice setup) |
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```Context ---> Question + (A) Answer (B) Distractor1 (C) Distractor2 (D) Distractor3``` |
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Please refer to our distractor generation model: https://huggingface.co/potsawee/t5-large-generation-race-Distractor |
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## Citation |
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```bibtex |
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@article{manakul2023mqag, |
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title={MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization}, |
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author={Manakul, Potsawee and Liusie, Adian and Gales, Mark JF}, |
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journal={arXiv preprint arXiv:2301.12307}, |
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year={2023} |
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} |
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``` |
