# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("henry931007/mfma")
model = AutoModelForSequenceClassification.from_pretrained("henry931007/mfma")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
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},
}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henry931007/mfma")