|
--- |
|
language: en |
|
widget: |
|
- text: a 1968 american independent horror film \\n What is Night of the Living Dead? |
|
--- |
|
|
|
# QA2Claim Model From ZeroFEC |
|
|
|
ZeroFEC is a faithful and interpetable factual error correction framework introduced in the paper [Zero-shot Faithful Factual Error Correction](https://aclanthology.org/2023.acl-long.311/). It involves a component that converts qa-pairs to declarative statements, which is hosted in this repo. The associated code is released in [this](https://github.com/khuangaf/ZeroFEC) repository. |
|
|
|
### How to use |
|
Using Huggingface pipeline abstraction: |
|
```python |
|
from transformers import pipeline |
|
|
|
nlp = pipeline("text2text-generation", model='khhuang/zerofec-qa2claim-t5-base', tokenizer='khhuang/zerofec-qa2claim-t5-base') |
|
|
|
QUESTION = "What is Night of the Living Dead?" |
|
ANSWER = "a 1968 american independent horror film" |
|
|
|
def format_inputs(question: str, answer: str): |
|
return f"{answer} \\n {question}" |
|
|
|
text = format_inputs(QUESTION, ANSWER) |
|
|
|
nlp(text) |
|
# should output [{'generated_text': 'Night of the Living Dead is a 1968 american independent horror film.'}] |
|
``` |
|
|
|
Using the pre-trained model directly: |
|
```python |
|
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
|
|
|
tokenizer = AutoTokenizer.from_pretrained('khhuang/zerofec-qa2claim-t5-base') |
|
model = AutoModelForSeq2SeqLM.from_pretrained('khhuang/zerofec-qa2claim-t5-base') |
|
|
|
QUESTION = "What is Night of the Living Dead?" |
|
ANSWER = "a 1968 american independent horror film" |
|
|
|
def format_inputs(question: str, answer: str): |
|
return f"{answer} \\n {question}" |
|
|
|
text = format_inputs(QUESTION, ANSWER) |
|
|
|
input_ids = tokenizer(text, return_tensors="pt").input_ids |
|
generated_ids = model.generate(input_ids, max_length=32, num_beams=4) |
|
output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) |
|
print(output) |
|
# should output "Night of the Living Dead is a 1968 american independent horror film." |
|
``` |
|
|
|
### Citation |
|
``` |
|
@inproceedings{huang-etal-2023-zero, |
|
title = "Zero-shot Faithful Factual Error Correction", |
|
author = "Huang, Kung-Hsiang and |
|
Chan, Hou Pong and |
|
Ji, Heng", |
|
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", |
|
month = jul, |
|
year = "2023", |
|
address = "Toronto, Canada", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://aclanthology.org/2023.acl-long.311", |
|
doi = "10.18653/v1/2023.acl-long.311", |
|
pages = "5660--5676", |
|
} |
|
``` |
