|
--- |
|
language: |
|
- en |
|
tags: |
|
- text2text-generation |
|
datasets: |
|
- domenicrosati/QA2D |
|
widget: |
|
- text: "Where in the world is Carmen Sandiego. She is in Abruzzo" |
|
example_title: "Where is Carmen Sandiego?" |
|
- text: "Halifax is a city in which province. Nova Scotia" |
|
example_title: "A Halifact" |
|
--- |
|
# Question-Answer to Statement Converter |
|
|
|
A question answer pair to statement converter from https://github.com/jifan-chen/QA-Verification-Via-NLI |
|
|
|
See: |
|
``` |
|
@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>`. |
|
|
|
```python |
|
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.']` |
|
|
