Update README.md

README.md

@@ -23,6 +23,64 @@ This model is a fine-tuned version of the Transformer architecture using a custo
 - **F1 Micro**: 0.8763
 - **Validation Set Size**: 7608 samples
 
+## Usage
+```python
+from transformers import DistilBertForSequenceClassification, PreTrainedTokenizerFast
+import torch
+
+# Load the tokenizer and model
+tokenizers = PreTrainedTokenizerFast.from_pretrained("FlukeTJ/distilbert-base-thai-sentiment")
+models = DistilBertForSequenceClassification.from_pretrained("FlukeTJ/distilbert-base-thai-sentiment")
+
+# Set device (GPU if available, else CPU)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+models = models.to(device)
+
+def predict_sentiment(text):
+    # Tokenize the input text without token_type_ids
+    inputs = tokenizers(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    inputs.pop("token_type_ids", None)  # Remove token_type_ids if present
+    
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    
+    # Make prediction
+    with torch.no_grad():
+        outputs = models(**inputs)
+    
+    # Get probabilities
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    
+    # Get the predicted class 
+    predicted_class = torch.argmax(probabilities, dim=1).item()
+    
+    # Map class to sentiment
+    sentiment_map = {1: "Neutral", 0: "Positive", 2: "Negative"}
+    predicted_sentiment = sentiment_map[predicted_class]
+    
+    # Get the confidence score
+    confidence = probabilities[0][predicted_class].item()
+    
+    return predicted_sentiment, confidence
+
+# Example usage
+texts = [
+    "สุดยอดดด"
+]
+
+for text in texts:
+    sentiment, confidence = predict_sentiment(text)
+    print(f"Text: {text}")
+    print(f"Predicted Sentiment: {sentiment}")
+    print(f"Confidence: {confidence:.2f}")
+
+# =============================
+# Result    
+# Text: สุดยอดดด
+# Predicted Sentiment: Positive
+# Confidence: 0.96
+# =============================
+```
+
 ## Model Description
 
 This model is based on a **DistilBERT** architecture with the following configuration: