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!!'
 
@@ -16,24 +16,40 @@ model-index:
       - task:
           type: text-classfication
         dataset:
-          name: Silly-Machine/TuPyE-Dataset
+          name: TuPyE-Dataset
           type: Silly-Machine/TuPyE-Dataset
         metrics:
-          - name: f1
-            type: f1
-            value: 64.59
-        source:
-          name: Open LLM Leaderboard
-          url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
+          - type: accuracy
+            value: 0.907
+            name: Accuracy
+            verified: true
+          - type: f1
+            value: 0.903
+            name: F1-score
+            verified: true
+          - type: precision
+            value: 0.901
+            name: Precision
+            verified: true
+          - type: recall
+            value: 0.907
+            name: Recall
+            verified: true 
 ---
 
 ## 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-Large-Multilabel is a fine-tuned BERT model designed specifically for multilabel classification of hate speech in Portuguese. 
+Derived from the [BERTimbau large](https://huggingface.co/neuralmind/bert-large-portuguese-cased), 
+TuPy-Large is a refined solution for addressing categorical hate speech concerns (ageism, aporophobia, body shame, capacitism, LGBTphobia, political, 
+racism, religious intolerance, misogyny, and xenophobia).
 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.
+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 [TuPy Hate Speech DataSet](https://huggingface.co/datasets/Silly-Machine/TuPyE-Dataset), sourced from diverse social networks.
 
 ## Available models
 
@@ -44,7 +60,7 @@ 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
@@ -76,5 +92,4 @@ def classify_hate_speech(model_name, text):
 model_name = "Silly-Machine/TuPy-Bert-Large-Multilabel"
 text = "Bom dia, flor do dia!!"
 classify_hate_speech(model_name, text)
- 
 ```