Update README.md

README.md

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 # 繁體中文情緒分類: 負面(0)、正面(1)
+    from transformers import AutoTokenizer, AutoModelForSequenceClassification
+    tokenizer = AutoTokenizer.from_pretrained("Cheng-Lung/bert-sentiment")
+    model = AutoModelForSequenceClassification.from_pretrained("Cheng-Lung/bert-sentiment")
+    
+    ## Pediction
+    target_names=['Negative','Positive']
+    max_length = 200 # 最多字數 若超出模型訓練時的字數，以模型最大字數為依據 
+    def get_sentiment_proba(text):
+        # prepare our text into tokenized sequence
+        inputs = tokenizer(text, padding=True, truncation=True, max_length=max_length, return_tensors="pt")
+        # perform inference to our model
+        outputs = model(**inputs)
+        # get output probabilities by doing softmax
+        probs = outputs[0].softmax(1)
+    
+        response = {'Negative': round(float(probs[0, 0]), 2), 'Positive': round(float(probs[0, 1]), 2)}
+        # executing argmax function to get the candidate label
+        #return probs.argmax()
+        return response
+    
+    get_sentiment_proba('不喜歡這款產品')