Update README.md

README.md

@@ -21,8 +21,25 @@ A BART (base) model trained to classify whether a summary is *faithful* to the o
 ## Usage
 Concatenate a summary and a source document as input (note that the summary needs to be the **first** sentence). 
 
+Here's an example usage (with PyTorch)
 ```python
-model = 
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+  
+tokenizer = AutoTokenizer.from_pretrained("CogComp/bart-faithful-summary-detector")
+model = AutoModelForSequenceClassification.from_pretrained("CogComp/bart-faithful-summary-detector")
+
+article = "Ban-Ki Moon was re-elected for a second term by the UN General Assembly, unopposed and unanimously, on 21 June 2011"
+
+bad_summary = "Ban Ki-moon was elected for a second term in 2007"
+good_summary = "Ban Ki-moon was elected for a second term in 2011"
+
+bad_pair = tokenizer(text=bad_summary, text_pair=article, return_tensors='pt')
+good_pair = tokenizer(text=good_summary, text_pair=article, return_tensors='pt')
+
+bad_score = model(**bad_pair)
+good_score = model(**good_pair)
+
+print(good_score[0][:, 1] > bad_score[0][:, 1]) # True, label mapping: "0" -> "Hallucinated" "1" -> "Faithful"
 ```