Update README.md

README.md

@@ -19,16 +19,20 @@ model-index:
           name: TuPyE-Dataset
           type: Silly-Machine/TuPyE-Dataset
         metrics:
+          - type: accuracy
+            value: 0.901
+            name: Accuracy
+            verified: true
           - type: f1
-            value: 0.84
+            value: 0.899
             name: F1-score
             verified: true
           - type: precision
-            value: 0.85
+            value: 0.897
             name: Precision
             verified: true
           - type: recall
-            value: 0.84
+            value: 0.901
             name: Recall
             verified: true 
 ---
@@ -36,10 +40,9 @@ model-index:
 ## Introduction
 
 
-Tupy-BERT-Base-Multilabel is a fine-tuned BERT model designed specifically for multilabel classification of hate speech in Portuguese. 
+Tupy-BERT-Base-Binary is a fine-tuned BERT model designed specifically for binary classification of hate speech in Portuguese. 
 Derived from the [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased), 
-TuPy-Base is a refined solution for addressing categorical hate speech concerns (ageism, aporophobia, body shame, capacitism, LGBTphobia, political, 
-racism, religious intolerance, misogyny, and xenophobia).
+TuPy-Base is a refined solution for addressing binary hate speech concerns (hate or not hate).
 For more details or specific inquiries, please refer to the [BERTimbau repository](https://github.com/neuralmind-ai/portuguese-bert/).
 
 The efficacy of Language Models can exhibit notable variations when confronted with a shift in domain between training and test data. 
@@ -85,7 +88,7 @@ def classify_hate_speech(model_name, text):
         print(f"{i + 1}) Label: {label} Score: {score:.4f}")
 
 # Example usage
-model_name = "Silly-Machine/TuPy-Bert-Base-Multilabel"
+model_name = "Silly-Machine/TuPy-Bert-Base-Binary-Classifier"
 text = "Bom dia, flor do dia!!"
 classify_hate_speech(model_name, text)