update model card README.md

README.md

@@ -22,16 +22,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9329479768786128
+      value: 0.9315407456285054
     - name: Recall
       type: recall
-      value: 0.9506900033658701
+      value: 0.9503534163581285
     - name: F1
       type: f1
-      value: 0.9417354338584647
+      value: 0.9408530489836722
     - name: Accuracy
       type: accuracy
-      value: 0.9863719314770119
+      value: 0.9861511744275033
 ---
 
 <!-- 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 [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0609
-- Precision: 0.9329
-- Recall: 0.9507
-- F1: 0.9417
-- Accuracy: 0.9864
+- Loss: 0.0615
+- Precision: 0.9315
+- Recall: 0.9504
+- F1: 0.9409
+- Accuracy: 0.9862
 
 ## Model description
 
@@ -76,9 +76,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.086         | 1.0   | 1756 | 0.0632          | 0.9255    | 0.9408 | 0.9331 | 0.9833   |
-| 0.0316        | 2.0   | 3512 | 0.0631          | 0.9252    | 0.9456 | 0.9353 | 0.9846   |
-| 0.0219        | 3.0   | 5268 | 0.0609          | 0.9329    | 0.9507 | 0.9417 | 0.9864   |
+| 0.084         | 1.0   | 1756 | 0.0683          | 0.9173    | 0.9347 | 0.9259 | 0.9826   |
+| 0.0342        | 2.0   | 3512 | 0.0602          | 0.9312    | 0.9470 | 0.9390 | 0.9856   |
+| 0.0236        | 3.0   | 5268 | 0.0615          | 0.9315    | 0.9504 | 0.9409 | 0.9862   |
 
 
 ### Framework versions