update model card README.md

README.md

@@ -22,16 +22,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9300908486594284
+      value: 0.9244616234124793
     - name: Recall
       type: recall
-      value: 0.9391430808815304
+      value: 0.9364582168027744
     - name: F1
       type: f1
-      value: 0.9345950459226274
+      value: 0.9304212515282871
     - name: Accuracy
       type: accuracy
-      value: 0.9842407104389407
+      value: 0.9833987322668276
 ---
 
 <!-- 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 conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0589
-- Precision: 0.9301
-- Recall: 0.9391
-- F1: 0.9346
-- Accuracy: 0.9842
+- Loss: 0.0623
+- Precision: 0.9245
+- Recall: 0.9365
+- F1: 0.9304
+- Accuracy: 0.9834
 
 ## Model description
 
@@ -76,9 +76,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.2402        | 1.0   | 878  | 0.0692          | 0.9177    | 0.9248 | 0.9213 | 0.9815   |
-| 0.0506        | 2.0   | 1756 | 0.0600          | 0.9249    | 0.9361 | 0.9305 | 0.9836   |
-| 0.0304        | 3.0   | 2634 | 0.0589          | 0.9301    | 0.9391 | 0.9346 | 0.9842   |
+| 0.2377        | 1.0   | 878  | 0.0711          | 0.9176    | 0.9254 | 0.9215 | 0.9813   |
+| 0.0514        | 2.0   | 1756 | 0.0637          | 0.9213    | 0.9346 | 0.9279 | 0.9831   |
+| 0.031         | 3.0   | 2634 | 0.0623          | 0.9245    | 0.9365 | 0.9304 | 0.9834   |
 
 
 ### Framework versions