Update README.md

README.md

@@ -16,25 +16,6 @@ pipeline_tag: text-classification
 # Model Card for Model ID
 
 
-## Model Details
-
-
-## How to Get Started with the Model
-
-    comment = ""
-    input = tokenizer(comment, return_tensors="pt", padding =True, truncation = True)
-    outputs = model(**{k: v.to("cuda") for k, v in input.items()})
-    logits = outputs.logits.detach().cpu().numpy()
-    
-    probs = np.exp(logits) / np.exp(logits).sum(axis=-1, keepdims=True)
-    
-    preds = np.argmax(probs, axis=1)
-    
-    for text, pred, p in zip(com, preds, probs):
-        label = id2label[int(pred)]
-        confidence = np.round(p[int(pred)], 3)
-        print(f"Text: {text}\nPredicted: {label} (Confidence: {confidence})\n")
-
 ## Training Details
 
 ### Training Data