End of training

README.md

@@ -1,6 +1,6 @@
 ---
 license: mit
-base_model: PORTULAN/albertina-100m-portuguese-ptpt-encoder
+base_model: neuralmind/bert-large-portuguese-cased
 tags:
 - generated_from_trainer
 metrics:
@@ -9,22 +9,22 @@ metrics:
 - precision
 - recall
 model-index:
-- name: LVI_albertina-100m-portuguese-ptpt-encoder
+- name: LVI_bert-large-portuguese-cased
   results: []
 ---
 
 <!-- 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. -->
 
-# LVI_albertina-100m-portuguese-ptpt-encoder
+# LVI_bert-large-portuguese-cased
 
-This model is a fine-tuned version of [PORTULAN/albertina-100m-portuguese-ptpt-encoder](https://huggingface.co/PORTULAN/albertina-100m-portuguese-ptpt-encoder) on the None dataset.
+This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6932
-- Accuracy: 0.5
-- F1: 0.0
-- Precision: 0.0
-- Recall: 0.0
+- Loss: 0.0755
+- Accuracy: 0.9775
+- F1: 0.9775
+- Precision: 0.9758
+- Recall: 0.9793
 
 ## Model description
 
@@ -43,7 +43,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-05
+- learning_rate: 5e-06
 - train_batch_size: 16
 - eval_batch_size: 16
 - seed: 42
@@ -53,15 +53,9 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 0.5678        | 1.0   | 3217  | 0.6316          | 0.6653   | 0.5619 | 0.8128    | 0.4294 |
-| 0.6042        | 2.0   | 6434  | 0.6911          | 0.5      | 0.0    | 0.0       | 0.0    |
-| 0.6946        | 3.0   | 9651  | 0.6932          | 0.5      | 0.0    | 0.0       | 0.0    |
-| 0.694         | 4.0   | 12868 | 0.6932          | 0.5      | 0.6667 | 0.5       | 1.0    |
-| 0.6942        | 5.0   | 16085 | 0.6933          | 0.5      | 0.6667 | 0.5       | 1.0    |
-| 0.6936        | 6.0   | 19302 | 0.6937          | 0.5      | 0.6667 | 0.5       | 1.0    |
-| 0.6937        | 7.0   | 22519 | 0.6932          | 0.5      | 0.0    | 0.0       | 0.0    |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.1071        | 1.0   | 3217 | 0.0755          | 0.9775   | 0.9775 | 0.9758    | 0.9793 |
 
 
 ### Framework versions