Update README.md

README.md

@@ -4,6 +4,55 @@ datasets:
 - haipradana/indonesian-twitter-hate-speech-cleaned
 language:
 - id
-base_model:
-- cardiffnlp/twitter-roberta-base-sentiment-latest
----
+tags:
+- bert
+- RoBERTa
+- tweet
+- hate
+- twitter
+---
+
+# Fine-tuned RoBERTa pre-trained model to classify Indonesian hate tweet(s)
+
+Just check GitHub for full-code and Google Colab: https://github.com/haipradana/RoBERTa-Indonesian-Hate-Tweet-Classification/tree/main
+
+This project fine-tunes a RoBERTa model from [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) to classify Indonesian tweets as either **neutral** or **hate speech**.
+
+## How to use this model?
+
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model
+tokenizer = AutoTokenizer.from_pretrained('./model')
+model = AutoModelForSequenceClassification.from_pretrained('./model')
+
+# Predict
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=511)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    prediction = torch.argmax(outputs.logits, dim=1).item()
+    return 'hate' if prediction == 1 else 'neutral'
+
+# Example
+result = predict("Paru-parumu terbuat dari batu ya? udah sakit gini masih aja merokok!")
+print(result)  # Output: hate
+```
+
+### Or just using the script in the GitHub Repos
+
+```bash
+cd scripts
+python predict.py
+```
+## Performance Metrics
+
+```
+Accuracy:  82.01%
+Precision: 82.68%
+Recall:    81.72%
+F1-Score:  82.19%
+```