Update README.md

README.md

@@ -106,25 +106,25 @@ This project was completed to explore the effectiveness of parameter-efficient f
 
 ### Usage
 
-usage:
-  - step_1: |
-      Log in to Hugging Face:
-      ```bash
-      huggingface-cli login
-      ```
-  - step_2: |
-      Load the model:
-      ```python
-      from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-      tokenizer = AutoTokenizer.from_pretrained("jamander/Project-Blockbuster")
-      model = AutoModelForSequenceClassification.from_pretrained("jamander/Project-Blockbuster")
-      ```
-  - step_3: |
-      Tokenize and predict:
-      ```python
-      inputs = tokenizer("I loved the movie!", return_tensors="pt", padding=True, truncation=True)
-      outputs = model(**inputs)
-      prediction = torch.argmax(outputs.logits, dim=-1)
-      print("Prediction:", "Positive" if prediction == 1 else "Negative")
-      ```
+  To use this model, follow these steps:
+
+  1. **Log in to Hugging Face**:
+     ```bash
+     huggingface-cli login
+     ```
+
+  2. **Load the model**:
+     ```python
+     from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+     tokenizer = AutoTokenizer.from_pretrained("jamander/Project-Blockbuster")
+     model = AutoModelForSequenceClassification.from_pretrained("jamander/Project-Blockbuster")
+     ```
+
+  3. **Tokenize and predict**:
+     ```python
+     inputs = tokenizer("I loved the movie!", return_tensors="pt", padding=True, truncation=True)
+     outputs = model(**inputs)
+     prediction = torch.argmax(outputs.logits, dim=-1)
+     print("Prediction:", "Positive" if prediction == 1 else "Negative")
+