Inria-CEDAR
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FactSpotter-DeBERTaV3-Large

Text Classification

Inference Endpoints

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guihu commited on Mar 19

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ad2066e

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@@ -30,8 +30,10 @@ def sentence_cls_score(input_strings, predicate_cls_model, predicate_cls_tokeniz
     softmax_cls_output = torch.softmax(prev_cls_output.logits, dim=1, )
     return softmax_cls_output
 tokenizer = AutoTokenizer.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Large")
 model = AutoModelForSequenceClassification.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Large")
 # pairs of texts (as premises) and triples (as hypotheses)
 cls_texts = [("the aarhus is the airport of aarhus, denmark", "aarhus airport | city served | aarhus, denmark"),

     softmax_cls_output = torch.softmax(prev_cls_output.logits, dim=1, )
     return softmax_cls_output
 tokenizer = AutoTokenizer.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Large")
 model = AutoModelForSequenceClassification.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Large")
+model.to(torch.device("cuda"))
 # pairs of texts (as premises) and triples (as hypotheses)
 cls_texts = [("the aarhus is the airport of aarhus, denmark", "aarhus airport | city served | aarhus, denmark"),