Update README.md

README.md

@@ -1,34 +1,39 @@
 ---
 language: en
 tags:
-  - emotion-detection
-  - text-classification
-  - transformers
-  - distilbert
+- emotion-detection
+- text-classification
+- transformers
+- distilbert
 datasets:
-  - nelgiriyewithana/emotions
+- nelgiriyewithana/emotions
 model-index:
-  - name: DistilBERT Emotion Classifier
-    results:
-      - task:
-          type: text-classification
-          name: Emotion Detection
-        dataset:
-          name: Kaggle Emotions Dataset
-          type: text
-        metrics:
-          - name: Accuracy
-            type: accuracy
-            value: 0.94
-          - name: F1
-            type: f1
-            value: 0.94
-          - name: Precision
-            type: precision
-            value: 0.94
-          - name: Recall
-            type: recall
-            value: 0.94
+- name: DistilBERT Emotion Classifier
+  results:
+  - task:
+      type: text-classification
+      name: Emotion Detection
+    dataset:
+      name: Kaggle Emotions Dataset
+      type: text
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.94
+    - name: F1
+      type: f1
+      value: 0.94
+    - name: Precision
+      type: precision
+      value: 0.94
+    - name: Recall
+      type: recall
+      value: 0.94
+license: apache-2.0
+metrics:
+- accuracy
+- recall
+- precision
 ---
 
 # DistilBERT Emotion Classifier 🎭
@@ -73,4 +78,4 @@ model = AutoModelForSequenceClassification.from_pretrained(model_name)
 nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
 
 print(nlp("I feel so happy and excited today!"))
-# [{'label': 'joy', 'score': 0.98}]
+# [{'label': 'joy', 'score': 0.98}]