Update README.md

README.md

@@ -25,9 +25,43 @@ It achieves the following results on the evaluation set:
 - Loss: 1.3109
 - Accuracy: 0.5962
 
-## Model description
-
-More information needed
+## 模型的使用
+
+from typing import Any
+import torch
+
+class MultipleChoicePipeline:
+    def __init__(self, model, tokenizer) -> None:
+        self.model = model
+        self.tokenizer = tokenizer
+        self.device = model.device
+
+    def preprocess(self, context, question, choices):
+        cs, qcs = [], []
+        for choice in choices:
+            cs.append(context)
+            qcs.append(question + " " + choice)
+        return tokenizer(cs, qcs, truncation="only_first", max_length=256, return_tensors="pt")
+
+    def predict(self, inputs):
+        inputs = {k: v.unsqueeze(0).to(self.device) for k, v in inputs.items()}
+        return self.model(**inputs).logits
+
+    def postprocess(self, logits, choices):
+        predition = torch.argmax(logits, dim=-1).cpu().item()
+        return choices[predition]
+
+    def __call__(self, context, question, choices) -> Any:
+        inputs = self.preprocess(context,question,choices)
+        logits = self.predict(inputs)
+        result = self.postprocess(logits, choices)
+        return result
+
+if __name__ == "__main__":
+  pipe = MultipleChoicePipeline(model, tokenizer)
+  result1 = pipe("國堂在台北上班","國堂在哪裏上班?",['台北','台中'])
+  print(result1)
+  
 
 ## Intended uses & limitations