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transformers1from transformers import AutoModelForSequenceClassification, AutoTokenizer
2model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/xlm-roberta-large-squad2-ctkfacts")
3tokenizer = AutoTokenizer.from_pretrained("ctu-aic/xlm-roberta-large-squad2-ctkfacts")sentence_transformers CrossEncoderCrossEncoder API and we recommend it for its usage.1from sentence_transformers.cross_encoder import CrossEncoder
2model = CrossEncoder('ctu-aic/xlm-roberta-large-squad2-ctkfacts')
3scores = model.predict([["My first context.", "My first hypothesis."],
4 ["Second context.", "Hypothesis."]])
@article{DBLP:journals/corr/abs-2201-11115,
author = {Jan Drchal and
Herbert Ullrich and
Martin R{'{y}}par and
Hana Vincourov{'{a}} and
V{'{a}}clav Moravec},
title = {CsFEVER and CTKFacts: Czech Datasets for Fact Verification},
journal = {CoRR},
volume = {abs/2201.11115},
year = {2022},
url = {https://arxiv.org/abs/2201.11115},
eprinttype = {arXiv},
eprint = {2201.11115},
timestamp = {Tue, 01 Feb 2022 14:59:01 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-11115.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}