update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9185779816513762
+      value: 0.930045871559633
     - name: F1
       type: f1
-      value: 0.9184998453245223
+      value: 0.9299971705127952
     - name: Precision
       type: precision
-      value: 0.9189738319234722
+      value: 0.9302394783826914
     - name: Recall
       type: recall
-      value: 0.9183190199545339
+      value: 0.9298749684263703
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2150
-- Accuracy: 0.9186
-- F1: 0.9185
-- Precision: 0.9190
-- Recall: 0.9183
+- Loss: 0.4216
+- Accuracy: 0.9300
+- F1: 0.9300
+- Precision: 0.9302
+- Recall: 0.9299
 
 ## Model description
 
@@ -74,14 +74,23 @@ The following hyperparameters were used during training:
 - total_train_batch_size: 640
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 1
+- num_epochs: 10
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 0.2397        | 1.0   | 105  | 0.2150          | 0.9186   | 0.9185 | 0.9190    | 0.9183 |
+| 0.2366        | 1.0   | 105  | 0.2193          | 0.9117   | 0.9115 | 0.9139    | 0.9111 |
+| 0.1104        | 2.0   | 210  | 0.2174          | 0.9243   | 0.9243 | 0.9243    | 0.9243 |
+| 0.0685        | 2.99  | 315  | 0.2441          | 0.9186   | 0.9185 | 0.9186    | 0.9185 |
+| 0.0476        | 4.0   | 421  | 0.2524          | 0.9232   | 0.9232 | 0.9233    | 0.9234 |
+| 0.0319        | 5.0   | 526  | 0.2832          | 0.9220   | 0.9219 | 0.9226    | 0.9217 |
+| 0.0227        | 6.0   | 631  | 0.3093          | 0.9289   | 0.9289 | 0.9289    | 0.9289 |
+| 0.0169        | 6.99  | 736  | 0.3755          | 0.9209   | 0.9209 | 0.9208    | 0.9210 |
+| 0.0112        | 8.0   | 842  | 0.3793          | 0.9220   | 0.9219 | 0.9234    | 0.9215 |
+| 0.0079        | 9.0   | 947  | 0.3980          | 0.9255   | 0.9254 | 0.9255    | 0.9254 |
+| 0.007         | 9.98  | 1050 | 0.4216          | 0.9300   | 0.9300 | 0.9302    | 0.9299 |
 
 
 ### Framework versions