Update README.md

README.md

@@ -6,7 +6,7 @@ language:
 - pt
 
 pipeline_tag: text-classification
-base_model: neuralmind/bert-base-portuguese-cased
+base_model: neuralmind/bert-large-portuguese-cased
 widget:
 - text: 'Bom dia, flor do dia!!'
 
@@ -20,19 +20,19 @@ model-index:
           type: Silly-Machine/TuPyE-Dataset
         metrics:
           - type: accuracy
-            value: 0.901
+            value: 0.907
             name: Accuracy
             verified: true
           - type: f1
-            value: 0.899
+            value: 0.903
             name: F1-score
             verified: true
           - type: precision
-            value: 0.897
+            value: 0.901
             name: Precision
             verified: true
           - type: recall
-            value: 0.901
+            value: 0.907
             name: Recall
             verified: true 
 ---
@@ -40,9 +40,9 @@ model-index:
 ## Introduction
 
 
-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 binary hate speech concerns (hate or not hate).
+TuPy-Bert-Large-Binary-Classifier 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-large-portuguese-cased), 
+TuPy-Bert-Large-Binary-Classifier 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. 
@@ -88,7 +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-Binary-Classifier"
+model_name = "Silly-Machine/TuPy-Bert-Large-Binary-Classifier"
 text = "Bom dia, flor do dia!!"
 classify_hate_speech(model_name, text)