@article{chen2021can,
title={Can NLI Models Verify QA Systems' Predictions?},
author={Chen, Jifan and Choi, Eunsol and Durrett, Greg},
journal={EMNLP Findings},
year={2021}
}
Note:
I am not the maintainer or orginal author just keeping it here to use huggingface APIs to produce statements from question answer pair for downstream applications.
TL;DR:
We fine-tune a seq2seq model,
T5-3B (Raffel et al., 2020), using the
(
a
,
q
,
d
)
pairs
annotated by Demszky et al. (2018).
Where a is answer, q is question, and d is declerative sentence (i.e. a statement).
See Appendex B.2 of Chen et al. for more.
Usage
The prompt should be
{question} {seperator} {answer}
where the seperator is
</s>
.
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained('domenicrosati/question_converter-3b')
model = AutoModelForSeq2SeqLM.from_pretrained('domenicrosati/question_converter-3b')
question = "Where in the world is Carmen Sandiego?"
answer = "She is in Abruzzo"
prompt = f'{question} </s> {answer}'
input_ids = tokenizer(prompt, return_tensors='pt').input_ids
output_ids = model.generate(input_ids)
responses = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
['Carmen Sandiego is in Abruzzo.']
Runs of domenicrosati question_converter-3b on huggingface.co
52
Total runs
4
24-hour runs
6
3-day runs
23
7-day runs
13
30-day runs
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