Update README.md

README.md

@@ -33,35 +33,31 @@ The model leverages the BertForSequenceClassification architecture, It has been
 
 ## Example 
 
-```from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import numpy as np
+from scipy.special import expit
+
+### Load the pre-trained model and tokenizer
+MODEL = "PavanDeepak/Topic_Classification"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+class_mapping = model.config.id2label
+
+### Example text
+text = "I love chicken manchuria"
+tokens = tokenizer(text, return_tensors="pt")
+output = model(**tokens)
+
+### Get scores and predictions
+scores = output.logits[0][0].detach().numpy()
+scores = expit(scores)
+predictions = (scores >= 0.5) * 1
+
+### Print predicted labels
+for i in range(len(predictions)):
+    if predictions[i]:
+        print(class_mapping[i])
 
-```from transformers import AutoTokenizer```
-
-```import numpy as np```
-
-```from scipy.special import expit```
-
-    
-```MODEL = f"PavanDeepak/Topic_Classification"```
-```tokenizer = AutoTokenizer.from_pretrained(MODEL)```
-
-```model = AutoModelForSequenceClassification.from_pretrained(MODEL)```
-```class_mapping = model.config.id2label```
-
-```text = "I love chicken manchuria"```
-```tokens = tokenizer(text, return_tensors='pt')```
-```output = model(**tokens)```
-
-```scores = output[0][0].detach().numpy()```
-```scores = expit(scores)```
-```predictions = (scores >= 0.5) * 1```
-
-
-```for i in range(len(predictions)):```
-
-  ```   if predictions[i]:```
-  
-    ```   print(class_mapping[i])```
 
 ## Output: