update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.5527638190954773
+      value: 0.5552523874488404
     - name: Recall
       type: recall
-      value: 0.4077849860982391
+      value: 0.37720111214087115
     - name: F1
       type: f1
-      value: 0.46933333333333327
+      value: 0.44922737306843263
     - name: Accuracy
       type: accuracy
-      value: 0.9475439271514685
+      value: 0.9469454063528707
 ---
 
 <!-- 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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3030
-- Precision: 0.5528
-- Recall: 0.4078
-- F1: 0.4693
-- Accuracy: 0.9475
+- Loss: 0.2942
+- Precision: 0.5553
+- Recall: 0.3772
+- F1: 0.4492
+- Accuracy: 0.9469
 
 ## Model description
 
@@ -78,11 +78,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 213  | 0.2577          | 0.5099    | 0.3568 | 0.4198 | 0.9437   |
-| No log        | 2.0   | 426  | 0.2675          | 0.5406    | 0.3948 | 0.4563 | 0.9463   |
-| 0.0737        | 3.0   | 639  | 0.3040          | 0.5737    | 0.3716 | 0.4511 | 0.9465   |
-| 0.0737        | 4.0   | 852  | 0.3042          | 0.5514    | 0.3976 | 0.4620 | 0.9474   |
-| 0.0307        | 5.0   | 1065 | 0.3030          | 0.5528    | 0.4078 | 0.4693 | 0.9475   |
+| No log        | 1.0   | 213  | 0.2666          | 0.6024    | 0.2808 | 0.3831 | 0.9405   |
+| No log        | 2.0   | 426  | 0.2605          | 0.5708    | 0.3364 | 0.4233 | 0.9456   |
+| 0.1299        | 3.0   | 639  | 0.2827          | 0.5658    | 0.3346 | 0.4205 | 0.9452   |
+| 0.1299        | 4.0   | 852  | 0.2836          | 0.5503    | 0.3753 | 0.4463 | 0.9469   |
+| 0.051         | 5.0   | 1065 | 0.2942          | 0.5553    | 0.3772 | 0.4492 | 0.9469   |
 
 
 ### Framework versions