---
license: apache-2.0
datasets:
- race
language:
- en
library_name: transformers
pipeline_tag: text2text-generation
inference: false
---
# t5-large fine-tuned to RACE for Generating Distractors
- Input: `question <sep> answer <sep> context`
- Output: list of 3 distractors

## Model Details

t5-large model is fine-tuned to the RACE dataset where the input is the concatenation of (question, answer, context) and the output is a list of 3 distractors. This is the second component in the question generation pipeline (i.e. `g2`) 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-Distractor")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-race-Distractor")

>>> 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", "")
>>> question = "What is the best title for the passage?"
>>> answer = "Djokovic's application for special permission to enter the United States"

>>> input_text = " ".join([question, tokenizer.sep_token, answer, tokenizer.sep_token, context])
>>> inputs = tokenizer(input_text, return_tensors="pt")
>>> outputs = model.generate(**inputs, max_new_tokens=128)
>>> distractors = tokenizer.decode(outputs[0], skip_special_tokens=False)
>>> distractors = distractors.replace(tokenizer.pad_token, "").replace(tokenizer.eos_token, "")
>>> distractors = [y.strip() for y in distractors.split(tokenizer.sep_token)]
>>> print(distractors)
['The United States has extended its requirement for international visitors to be vaccinated against Covid-19',
"Djokovic's return to the ATP tour in Dubai",
"Djokovic's hope for a positive decision to allow him to play at Indian Wells and the Miami Open"]

```


## 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}
}
```