Update README.md

README.md

@@ -19,9 +19,18 @@ model-index:
           name: Silly-Machine/TuPyE-Dataset
           type: Silly-Machine/TuPyE-Dataset
         metrics:
-          - name: f1
-            type: f1
-            value: 64.59
+          - type: accuracy
+            value: 0.8
+            name: Accuracy
+            verified: true
+          - type: f1
+            value: 0.759
+            name: F1
+            verified: true
+          - type: roc_auc
+            value: 0.891
+            name: AUC
+            verified: true
         source:
           name: Open LLM Leaderboard
           url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
@@ -30,7 +39,7 @@ model-index:
 ## Introduction
 
 
-Tupi-BERT-Base 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), TuPi-Base is refinde solution for addressing hate speech concerns.
+Tupy-BERT-Base 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 hate speech concerns.
 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. In the creation of a specialized Portuguese Language Model tailored for hate speech classification, the original BERTimbau model underwent fine-tuning processe carried out on the [TuPi Hate Speech DataSet](https://huggingface.co/datasets/FpOliveira/TuPi-Portuguese-Hate-Speech-Dataset-Binary), sourced from diverse social networks.
@@ -44,7 +53,7 @@ The efficacy of Language Models can exhibit notable variations when confronted w
 | `Silly-Machine/TuPy-Bert-Base-Multilabel` | BERT-Base | 12      | 109M    |
 | `Silly-Machine/TuPy-Bert-Large-Multilabel` | BERT-Large | 24      | 334M    |
 
-## Example usage usage
+## Example usage
 
 ```python
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig