update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.6388059701492538
+      value: 0.6239782016348774
     - name: Recall
       type: recall
-      value: 0.39666357738646896
+      value: 0.42446709916589437
     - name: F1
       type: f1
-      value: 0.48942252715837625
+      value: 0.5052399338113625
     - name: Accuracy
       type: accuracy
-      value: 0.9491257321191912
+      value: 0.9504082766876149
 ---
 
 <!-- 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-large-uncased](https://huggingface.co/bert-large-uncased) on the wnut_17 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2724
-- Precision: 0.6388
-- Recall: 0.3967
-- F1: 0.4894
-- Accuracy: 0.9491
+- Loss: 0.2851
+- Precision: 0.6240
+- Recall: 0.4245
+- F1: 0.5052
+- Accuracy: 0.9504
 
 ## Model description
 
@@ -72,14 +72,15 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 2
+- num_epochs: 3
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 213  | 0.2544          | 0.6019    | 0.3503 | 0.4429 | 0.9460   |
-| No log        | 2.0   | 426  | 0.2724          | 0.6388    | 0.3967 | 0.4894 | 0.9491   |
+| No log        | 1.0   | 213  | 0.2551          | 0.6727    | 0.3105 | 0.4249 | 0.9446   |
+| No log        | 2.0   | 426  | 0.2521          | 0.6491    | 0.4235 | 0.5126 | 0.9510   |
+| 0.1128        | 3.0   | 639  | 0.2851          | 0.6240    | 0.4245 | 0.5052 | 0.9504   |
 
 
 ### Framework versions