update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.4262295081967213
+      value: 0.5251322751322751
     - name: Recall
       type: recall
-      value: 0.1927710843373494
+      value: 0.36793327154772937
     - name: F1
       type: f1
-      value: 0.26547543075941293
+      value: 0.43269754768392366
     - name: Accuracy
       type: accuracy
-      value: 0.9353597537514429
+      value: 0.9450643409858492
 ---
 
 <!-- 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: 0.2958
-- Precision: 0.4262
-- Recall: 0.1928
-- F1: 0.2655
-- Accuracy: 0.9354
+- Loss: 0.2693
+- Precision: 0.5251
+- Recall: 0.3679
+- F1: 0.4327
+- Accuracy: 0.9451
 
 ## Model description
 
@@ -72,14 +72,17 @@ 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: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 107  | 0.3133          | 0.3077    | 0.0556 | 0.0942 | 0.9298   |
-| No log        | 2.0   | 214  | 0.2958          | 0.4262    | 0.1928 | 0.2655 | 0.9354   |
+| No log        | 1.0   | 107  | 0.3088          | 0.3506    | 0.1446 | 0.2047 | 0.9328   |
+| No log        | 2.0   | 214  | 0.2634          | 0.5403    | 0.3170 | 0.3995 | 0.9414   |
+| No log        | 3.0   | 321  | 0.2530          | 0.5282    | 0.3559 | 0.4252 | 0.9435   |
+| No log        | 4.0   | 428  | 0.2587          | 0.5206    | 0.3753 | 0.4362 | 0.9446   |
+| 0.1695        | 5.0   | 535  | 0.2693          | 0.5251    | 0.3679 | 0.4327 | 0.9451   |
 
 
 ### Framework versions