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Browse files
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+ ---
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+ language: en
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+ widget:
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+ - text: a 1968 american independent horror film \\n What is Night of the Living Dead?
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+ ---
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+
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+ # QA2Claim Model From ZeroFEC
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+
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+ 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.
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+
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+ ### How to use
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+ Using Huggingface pipeline abstraction:
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+ ```python
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+ from transformers import pipeline
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+
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+ nlp = pipeline("text2text-generation", model='khhuang/zerofec-qa2claim-t5-base', tokenizer='khhuang/zerofec-qa2claim-t5-base')
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+
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+ QUESTION = "What is Night of the Living Dead?"
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+ ANSWER = "a 1968 american independent horror film"
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+
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+ def format_inputs(question: str, answer: str):
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+ return f"{answer} \\n {question}"
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+
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+ text = format_inputs(QUESTION, ANSWER)
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+
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+ nlp(text)
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+ # should output [{'generated_text': 'Night of the Living Dead is a 1968 american independent horror film.'}]
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+ ```
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+
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+ Using the pre-trained model directly:
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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+
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+ tokenizer = AutoTokenizer.from_pretrained('khhuang/zerofec-qa2claim-t5-base')
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+ model = AutoModelForSeq2SeqLM.from_pretrained('khhuang/zerofec-qa2claim-t5-base')
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+
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+ QUESTION = "What is Night of the Living Dead?"
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+ ANSWER = "a 1968 american independent horror film"
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+
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+ def format_inputs(question: str, answer: str):
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+ return f"{answer} \\n {question}"
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+
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+ text = format_inputs(QUESTION, ANSWER)
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+
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+ input_ids = tokenizer(text, return_tensors="pt").input_ids
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+ generated_ids = model.generate(input_ids, max_length=32, num_beams=4)
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+ output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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+ print(output)
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+ # should output "Night of the Living Dead is a 1968 american independent horror film."
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+ ```
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+
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+ ### Citation
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+ ```
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+ @inproceedings{huang-etal-2023-zero,
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+ title = "Zero-shot Faithful Factual Error Correction",
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+ author = "Huang, Kung-Hsiang and
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+ Chan, Hou Pong and
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+ Ji, Heng",
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+ booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
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+ month = jul,
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+ year = "2023",
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+ address = "Toronto, Canada",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://aclanthology.org/2023.acl-long.311",
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+ doi = "10.18653/v1/2023.acl-long.311",
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+ pages = "5660--5676",
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+ }
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+ ```
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Binary file (3.31 kB). View file