Update README.md

README.md

@@ -11,7 +11,7 @@ language:
 
 This model is related to the paper **"FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation"**. 
 
-Given a triple of format "subject | predicate | object" and a text, the model determines if the triple is present in the text. 
+Given a triple of format "subject, predicate, object" and a text, the model determines if the triple is present in the text. 
 
 Different from the paper using ELECTRA, this model is finetuned on DeBERTaV3. 
 
@@ -37,8 +37,8 @@ model = AutoModelForSequenceClassification.from_pretrained("Inria-CEDAR/FactSpot
 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")]
+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")]
 cls_scores = sentence_cls_score(cls_texts, model, tokenizer)
 # Dimensions: 0-entailment, 1-neutral, 2-contradiction
 label_names = ["entailment", "neutral", "contradiction"]