update model card README.md

README.md

@@ -22,16 +22,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.8219254879975536
+      value: 0.8210081035625095
     - name: F1
       type: f1
-      value: 0.8151998307079064
+      value: 0.8181860806077187
     - name: Recall
       type: recall
-      value: 0.8219254879975536
+      value: 0.8210081035625095
     - name: Precision
       type: precision
-      value: 0.8165753119578384
+      value: 0.8187295355668672
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,11 +41,11 @@ 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 consumer-finance-complaints dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5451
-- Accuracy: 0.8219
-- F1: 0.8152
-- Recall: 0.8219
-- Precision: 0.8166
+- Loss: 0.5521
+- Accuracy: 0.8210
+- F1: 0.8182
+- Recall: 0.8210
+- Precision: 0.8187
 
 ## Model description
 
@@ -78,15 +78,8 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
-| 1.0678        | 0.2   | 500  | 0.9935          | 0.7193   | 0.6715 | 0.7193 | 0.6348    |
-| 0.8447        | 0.41  | 1000 | 0.8331          | 0.7468   | 0.7108 | 0.7468 | 0.6990    |
-| 0.7913        | 0.61  | 1500 | 0.7022          | 0.7770   | 0.7457 | 0.7770 | 0.7685    |
-| 0.6973        | 0.82  | 2000 | 0.6584          | 0.7922   | 0.7710 | 0.7922 | 0.7849    |
-| 0.5572        | 1.02  | 2500 | 0.6034          | 0.8076   | 0.7986 | 0.8076 | 0.7994    |
-| 0.5528        | 1.22  | 3000 | 0.6017          | 0.8085   | 0.7986 | 0.8085 | 0.8063    |
-| 0.5435        | 1.43  | 3500 | 0.5721          | 0.8147   | 0.8085 | 0.8147 | 0.8107    |
-| 0.4995        | 1.63  | 4000 | 0.5598          | 0.8161   | 0.8125 | 0.8161 | 0.8144    |
-| 0.4854        | 1.83  | 4500 | 0.5451          | 0.8219   | 0.8152 | 0.8219 | 0.8166    |
+| 0.7063        | 0.82  | 2000 | 0.6899          | 0.7809   | 0.7544 | 0.7809 | 0.7646    |
+| 0.505         | 1.63  | 4000 | 0.5521          | 0.8210   | 0.8182 | 0.8210 | 0.8187    |
 
 
 ### Framework versions