Update README.md

README.md

@@ -19,6 +19,8 @@ Different from the paper using ELECTRA, this model is finetuned on DeBERTaV3.
 
 ```
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+
 def sentence_cls_score(input_strings, predicate_cls_model, predicate_cls_tokenizer):
     tokenized_cls_input = predicate_cls_tokenizer(input_strings, truncation=True, padding=True,
                                                   return_token_type_ids=True)
@@ -28,9 +30,12 @@ def sentence_cls_score(input_strings, predicate_cls_model, predicate_cls_tokeniz
     prev_cls_output = predicate_cls_model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
     softmax_cls_output = torch.softmax(prev_cls_output.logits, dim=1, )
     return softmax_cls_output
+
+
 tokenizer = AutoTokenizer.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Small")
 model = AutoModelForSequenceClassification.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Small")
 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"),
              ("aarhus airport is 25.0 metres above the sea level", "aarhus airport | elevation above the sea level | 1174")]