update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.5302197802197802
+      value: 0.5348837209302325
     - name: Recall
       type: recall
-      value: 0.3577386468952734
+      value: 0.3410565338276182
     - name: F1
       type: f1
-      value: 0.42722744881018265
+      value: 0.4165251839275608
     - name: Accuracy
       type: accuracy
-      value: 0.945320849899534
+      value: 0.944636826129708
 ---
 
 <!-- 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: 0.2636
-- Precision: 0.5302
-- Recall: 0.3577
-- F1: 0.4272
-- Accuracy: 0.9453
+- Loss: 0.2714
+- Precision: 0.5349
+- Recall: 0.3411
+- F1: 0.4165
+- Accuracy: 0.9446
 
 ## 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   | 107  | 0.3099          | 0.3522    | 0.1214 | 0.1806 | 0.9321   |
-| No log        | 2.0   | 214  | 0.2670          | 0.5139    | 0.3086 | 0.3856 | 0.9410   |
-| No log        | 3.0   | 321  | 0.2547          | 0.4954    | 0.3466 | 0.4079 | 0.9426   |
-| No log        | 4.0   | 428  | 0.2553          | 0.5337    | 0.3596 | 0.4297 | 0.9452   |
-| 0.1736        | 5.0   | 535  | 0.2636          | 0.5302    | 0.3577 | 0.4272 | 0.9453   |
+| No log        | 1.0   | 107  | 0.3073          | 0.3474    | 0.1223 | 0.1809 | 0.9325   |
+| No log        | 2.0   | 214  | 0.2605          | 0.4597    | 0.2956 | 0.3598 | 0.9409   |
+| No log        | 3.0   | 321  | 0.2563          | 0.5319    | 0.3318 | 0.4087 | 0.9431   |
+| No log        | 4.0   | 428  | 0.2581          | 0.5255    | 0.3531 | 0.4224 | 0.9449   |
+| 0.1669        | 5.0   | 535  | 0.2714          | 0.5349    | 0.3411 | 0.4165 | 0.9446   |
 
 
 ### Framework versions