Update README.md

README.md

@@ -26,5 +26,25 @@ See the paper [Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Quest
 
 # Example use
 
-TODO
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from torch.nn import functional as F
+
+tokenizer = AutoTokenizer.from_pretrained("kortukov/answer-equivalence-bem")
+model = AutoModelForSequenceClassification.from_pretrained("kortukov/answer-equivalence-bem")
+
+question = "What does Ban Bossy encourage?"
+reference = "leadership in girls"
+candidate = "positions of power"
+
+def tokenize_function(question, reference, candidate):
+    text = f"[CLS] {candidate} [SEP]"
+    text_pair = f"{reference} [SEP] {question} [SEP]"
+    return tokenizer(text=text, text_pair=text_pair, add_special_tokens=False, padding='max_length', truncation=True, return_tensors='pt')
+
+inputs = tokenize_function(question, reference, candidate)
+out = model(**inputs)
+
+prediction = F.softmax(out.logits, dim=-1).argmax().item()
+```