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README.md
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This model is related to the paper **"FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation"**.
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Given a triple of format "subject
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Different from the paper using ELECTRA, this model is finetuned on DeBERTaV3.
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model.to(torch.device("cuda"))
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# pairs of texts (as premises) and triples (as hypotheses)
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cls_texts = [("the aarhus is the airport of aarhus, denmark", "aarhus airport
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("aarhus airport is 25.0 metres above the sea level", "aarhus airport
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cls_scores = sentence_cls_score(cls_texts, model, tokenizer)
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# Dimensions: 0-entailment, 1-neutral, 2-contradiction
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label_names = ["entailment", "neutral", "contradiction"]
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This model is related to the paper **"FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation"**.
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Given a triple of format "subject, predicate, object" and a text, the model determines if the triple is present in the text.
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Different from the paper using ELECTRA, this model is finetuned on DeBERTaV3.
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model.to(torch.device("cuda"))
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# pairs of texts (as premises) and triples (as hypotheses)
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cls_texts = [("the aarhus is the airport of aarhus, denmark", "aarhus airport, city served, aarhus, denmark"),
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("aarhus airport is 25.0 metres above the sea level", "aarhus airport, elevation above the sea level, 1174")]
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cls_scores = sentence_cls_score(cls_texts, model, tokenizer)
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# Dimensions: 0-entailment, 1-neutral, 2-contradiction
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label_names = ["entailment", "neutral", "contradiction"]
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