Add usage example

README.md

@@ -35,6 +35,38 @@ It achieves the following results on the evaluation set:
 - Loss: 0.6057
 - Accuracy: 0.8569
 
+## Example
+
+```
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("nfliu/roberta-large_boolq")
+tokenizer = AutoTokenizer.from_pretrained("nfliu/roberta-large_boolq")
+
+# Each example is a (question, context) pair.
+examples = [
+    ("Lake Tahoe is in California", "Lake Tahoe is a popular tourist spot in California."),
+    ("Water is wet", "Contrary to popular belief, water is not wet.")
+]
+
+encoded_input = tokenizer(examples, padding=True, truncation=True, return_tensors="pt")
+
+with torch.no_grad():
+    model_output = model(**encoded_input)
+    probabilities = torch.softmax(model_output.logits, dim=-1).cpu().tolist()
+
+probability_no = [round(prob[0], 2) for prob in probabilities]
+probability_yes = [round(prob[1], 2) for prob in probabilities]
+
+for example, p_no, p_yes in zip(examples, probability_no, probability_yes):
+    print(f"Question: {example[0]}")
+    print(f"Context: {example[1]}")
+    print(f"p(No | question, context): {p_no}")
+    print(f"p(Yes | question, context): {p_yes}")
+    print()
+```
+
 ## Model description
 
 More information needed