Update README.md

README.md

@@ -1,40 +1,42 @@
 ---
 license: mit
 tags:
-  - indobert
-  - emotion-classification
-  - text-classification
-  - indonesian
-  - torch
+- indobert
+- emotion-classification
+- text-classification
+- indonesian
+- torch
 language:
-  - id
+- id
 datasets:
-  - PRDECT-ID
+- PRDECT-ID
 model-index:
-  - name: IndoBERT Emotion Classification (5-Class)
-    results:
-      - task:
-          type: text-classification
-          name: Emotion Classification
-        dataset:
-          name: PRDECT-ID
-          type: text
-          description: >
-            A dataset of Indonesian product reviews labeled with five emotion categories:
-            love, happiness, anger, fear, and sadness.
-        metrics:
-          - name: Accuracy
-            type: accuracy
-            value: 0.7167
-          - name: F1 Score
-            type: f1
-            value: 0.7125
-          - name: Precision
-            type: precision
-            value: 0.7179
-          - name: Recall
-            type: recall
-            value: 0.7167
+- name: IndoBERT Emotion Classification (5-Class)
+  results:
+  - task:
+      type: text-classification
+      name: Emotion Classification
+    dataset:
+      name: PRDECT-ID
+      type: text
+      description: >
+        A dataset of Indonesian product reviews labeled with five emotion
+        categories: love, happiness, anger, fear, and sadness.
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7167
+    - name: F1 Score
+      type: f1
+      value: 0.7125
+    - name: Precision
+      type: precision
+      value: 0.7179
+    - name: Recall
+      type: recall
+      value: 0.7167
+base_model:
+- indobenchmark/indobert-base-p1
 ---
 
 # IndoBERT Emotion Classification (5-Class)
@@ -80,4 +82,4 @@ tokenizer = AutoTokenizer.from_pretrained("galennolan/indobert-b-p1-indoemotion-
 
 emotion_classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
 
-emotion_classifier("Produk ini bikin aku senang banget!")
+emotion_classifier("Produk ini bikin aku senang banget!")