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f850e8f a535623 ad7b949 e52615a 9df92fa 930f5d9 bbf9d53 9df92fa 701f991 9df92fa 930f5d9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## Pre-trained factual consistency checking model for abstractive summaries introduced in the following NAACL-22 paper.
from transformers import AutoModelforSequenceClassification
model = AutoModelforSequenceClassification("henry931007/mfma")
```
@inproceedings{lee2022mfma,
title={Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking},
author={Hwanhee Lee and Kang Min Yoo and Joonsuk Park and Hwaran Lee and Kyomin Jung},
year={2022},
month={july},
booktitle={Findings of the Association for Computational Linguistics: NAACL 2022},
}
``` |