Update README.md

README.md

@@ -1,38 +1,32 @@
 ---
 license: mit
-base_model: prajjwal1/bert-tiny
-tags:
-- generated_from_trainer
+datasets:
+- LemeExploreNau/VeraCruz
+language:
+- pt
 metrics:
 - accuracy
-model-index:
-- name: PtBr_Classifier2
-  results: []
+tags:
+- Portuguese
+- Brazilian
+- Language Classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# PtBr_Classifier2
+# PeroVazPT-PTBR Classifier
+
+## Model Description
+The PeroVazPT-PTBR Classifier is designed to classify text between European Portuguese (PT-PT) and Brazilian Portuguese (PT-BR).
 
-This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the None dataset.
+This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the [VeraCruz Dataset](https://huggingface.co/datasets/LemeExploreNau/VeraCruz).
 It achieves the following results on the evaluation set:
 - Loss: 0.1791
 - Accuracy: 0.9461
 
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+## Training Data
+The model was trained on the [VeraCruz Dataset](https://huggingface.co/datasets/LemeExploreNau/VeraCruz), a collection of text samples from both languages. The model was trained on a total of 500,000 examples, a evenly split between European Portuguese and Brazilian Portuguese, ensuring a balanced representation of both language variants.
 
 ### Training hyperparameters
 
@@ -55,10 +49,6 @@ The following hyperparameters were used during training:
 | 0.3122        | 0.19  | 1500 | 0.2578          | 0.9014   |
 | 0.2975        | 0.25  | 2000 | 0.1992          | 0.9396   |
 | 0.2877        | 0.31  | 2500 | 0.1791          | 0.9461   |
-| 0.2797        | 0.38  | 3000 | 0.1953          | 0.9350   |
-| 0.2714        | 0.44  | 3500 | 0.2240          | 0.9182   |
-| 0.2678        | 0.5   | 4000 | 0.2097          | 0.9320   |
-
 
 ### Framework versions