Update README.md

README.md

@@ -47,4 +47,53 @@ max_length = 128
 
 batch_size = 32
 
-eval_batch_size = 32
+eval_batch_size = 32
+
+## 🤖 How to use
+
+```python
+import tensorflow as tf
+from transformers import TFAutoModelForSequenceClassification, AutoTokenizer
+
+# Load model dan tokenizer
+model_name = "path/to/your/fine-tuned-model"  # Ganti dengan path model yang telah disimpan
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Fungsi untuk melakukan prediksi sentimen
+def predict(text):
+    sentiment_mapping = {
+        1: "positive",
+        0: "negative",
+        2: "neutral"
+    }
+    
+    # Tokenisasi teks
+    inputs = tokenizer(
+        text,
+        return_tensors="tf",
+        truncation=True,
+        padding="max_length",
+        max_length=128
+    )
+    
+    # Prediksi menggunakan model
+    outputs = model(**inputs)
+    logits = outputs.logits
+
+    # Menghitung probabilitas
+    probabilities = tf.nn.softmax(logits).numpy()
+    
+    # Menentukan label prediksi
+    predicted_index = int(tf.argmax(probabilities, axis=1).numpy()[0])
+    predicted_label = sentiment_mapping.get(predicted_index, "unknown")
+    
+    # Keyakinan prediksi
+    confidence = probabilities[0][predicted_index]
+
+    print(f"Teks: {text}")
+    print(f"Prediksi label: {predicted_label} (Confidence: {confidence:.2f})")
+
+# Contoh penggunaan
+text = "aku sedang jalan-jalan di Yogyakarta"
+predict(text)