Update README.md

README.md

@@ -57,36 +57,36 @@ Always evaluate the model on your target domain.
 
 ## How to Get Started with the Model
 
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-model_id = "gameresearch/modernbert-eie-2048"
-threshold = 0.2  # optimized decision threshold for EIE
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForSequenceClassification.from_pretrained(model_id)
-
-def predict_eie(review_text: str):
-    inputs = tokenizer(
-        review_text,
-        truncation=True,
-        padding="max_length",
-        max_length=2048,
-        return_tensors="pt"
-    )
-    with torch.no_grad():
-        outputs = model(**inputs)
-        logits = outputs.logits.squeeze()
-        probs = torch.softmax(logits, dim=-1)
-        prob_eie = probs[1].item()
-        label = int(prob_eie >= threshold)
-    return {
-        "prob_eie": prob_eie,
-        "label": label  # 1 = EIE present, 0 = EIE absent
-}
-
-example = "This game absolutely destroyed me emotionally. I still think about the ending."
-print(predict_eie(example))
+  from transformers import AutoTokenizer, AutoModelForSequenceClassification
+  import torch
+  
+  model_id = "gameresearch/modernbert-eie-2048"
+  threshold = 0.2  # optimized decision threshold for EIE
+  
+  tokenizer = AutoTokenizer.from_pretrained(model_id)
+  model = AutoModelForSequenceClassification.from_pretrained(model_id)
+  
+  def predict_eie(review_text: str):
+      inputs = tokenizer(
+          review_text,
+          truncation=True,
+          padding="max_length",
+          max_length=2048,
+          return_tensors="pt"
+      )
+      with torch.no_grad():
+          outputs = model(**inputs)
+          logits = outputs.logits.squeeze()
+          probs = torch.softmax(logits, dim=-1)
+          prob_eie = probs[1].item()
+          label = int(prob_eie >= threshold)
+      return {
+          "prob_eie": prob_eie,
+          "label": label  # 1 = EIE present, 0 = EIE absent
+  }
+  
+  example = "This game absolutely destroyed me emotionally. I still think about the ending."
+  print(predict_eie(example))
 
 
 ## Training Details