| ## 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}, | |
| } | |
| ``` |