Update README.md

README.md

@@ -1,3 +1,71 @@
----
-license: mit
----
+---
+license: mit
+language:
+- id
+metrics:
+- accuracy
+- f1
+base_model:
+- indobenchmark/indobert-base-p1
+tags:
+- sarcasm
+- satire
+- bert
+- indobert
+---
+# Fine-tuned indoBERT pre-trained model for sarcasm and satire classification
+
+Just check GitHub for full-code and Google Colab: https://github.com/haipradana/indobert-indonesia-sarcastic-satire-classification
+
+## How to use this model?
+
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load model
+tokenizer = AutoTokenizer.from_pretrained("indobert-indonesia-sarcastic-satire-classification/model")
+model = AutoModelForSequenceClassification.from_pretrained("indobert-indonesia-sarcastic-satire-classification/model")
+
+# Predict
+def predict(text: str):
+    inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    prediction = torch.argmax(logits, dim=1).item()
+    return 'sarcasm' if prediction == 1 else 'not sarcasm'
+
+# Example
+result = predict("Kamu penulis ya? pandai sekali mengarang cerita")
+print(result) #output = sarcasm
+```
+
+### Or just using the script in the GitHub Repos
+
+```bash
+cd scripts
+python predict.py
+```
+
+## Evaluation Results
+
+The model was evaluated on the test set with the following metrics:
+
+| Metric     | Value   |
+|------------|---------|
+| Accuracy   | 0.8378  |
+| Precision  | 0.8405  |
+| Recall     | 0.8286  |
+| F1-Score   | 0.8345  |
+
+### Training History
+
+| Epoch | Train Loss | Val Loss | Accuracy | Precision | F1-Score | Recall  |
+|-------|------------|----------|----------|-----------|----------|---------|
+| 1     | 0.4559     | 0.3512   | 0.8409   | 0.9022    | 0.8261   | 0.7618  |
+| 2     | 0.2491     | 0.3924   | 0.8339   | 0.7835    | 0.8459   | 0.9190  |
+| 3     | 0.1198     | 0.5980   | 0.8429   | 0.8188    | 0.8471   | 0.8774  |
+| 4     | 0.0439     | 0.9497   | 0.8444   | 0.8231    | 0.8479   | 0.8742  |
+| 5     | 0.0097     | 0.9962   | 0.8522   | 0.8421    | 0.8529   | 0.8640  |