Update README.md

README.md

@@ -154,23 +154,44 @@ TrainingArguments(
 ### ✅ Evaluation Results
 
 ```
-eval_accuracy  = 0.9749
-eval_precision = 0.9749
-eval_recall    = 0.9749
-eval_f1        = 0.9749
+
+eval_loss =                  0.10230540484189987
+eval_accuracy =              0.9749255130394028
+eval_precision =             0.9749490510899772
+eval_recall =                0.9749255130394028
+eval_f1 =                    0.9749326327197978
+eval_runtime =               71.9098
+eval_samples_per_second =    415.395
+eval_steps_per_second =      1.627
+epoch =                      5.0
+
 ```
 
 ---
 
 ## 🔎 Usage
 
+```python
+from transformers import pipeline
+
+pretrained_name = "ihsan31415/indo-roBERTa-financial-sentiment"
+
+nlp = pipeline(
+    "sentiment-analysis",
+    model=pretrained_name,
+    tokenizer=pretrained_name
+)
+
+nlp("IHSG diprediksi melemah karena sentimen global negatif")
+```
+
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
 # Load model and tokenizer
-model = AutoModelForSequenceClassification.from_pretrained("ihsan31415/finetuned-indo-roBERTa-financial-sentiment")
-tokenizer = AutoTokenizer.from_pretrained("ihsan31415/finetuned-indo-roBERTa-financial-sentiment")
+model = AutoModelForSequenceClassification.from_pretrained("ihsan31415/indo-roBERTa-financial-sentiment")
+tokenizer = AutoTokenizer.from_pretrained("ihsan31415/indo-roBERTa-financial-sentiment")
 
 # Example input
 text = "IHSG diprediksi melemah karena sentimen global negatif"