Update README.md
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README.md
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@@ -30,6 +30,7 @@ def sentence_cls_score(input_strings, predicate_cls_model, predicate_cls_tokeniz
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return softmax_cls_output
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tokenizer = AutoTokenizer.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Small")
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model = AutoModelForSequenceClassification.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Small")
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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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return softmax_cls_output
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tokenizer = AutoTokenizer.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Small")
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model = AutoModelForSequenceClassification.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Small")
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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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