update model card README.md

README.md

@@ -8,6 +8,7 @@ metrics:
 - precision
 - recall
 - f1
+- accuracy
 model-index:
 - name: bert-finetuned-ner
   results:
@@ -23,13 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9386095901775344
+      value: 0.9378943872467619
     - name: Recall
       type: recall
-      value: 0.9520363513968361
+      value: 0.9505217098619994
     - name: F1
       type: f1
-      value: 0.9452752945108196
+      value: 0.9441658308258107
+    - name: Accuracy
+      type: accuracy
+      value: 0.9862689115205746
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -39,11 +43,11 @@ 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: 0.0856
-- Precision: 0.9386
-- Recall: 0.9520
-- F1: 0.9453
-- Acurracy: 0.9869
+- Loss: 0.0635
+- Precision: 0.9379
+- Recall: 0.9505
+- F1: 0.9442
+- Accuracy: 0.9863
 
 ## Model description
 
@@ -72,11 +76,11 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Acurracy |
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0093        | 1.0   | 1756 | 0.1290          | 0.8910    | 0.9229 | 0.9067 | 0.9790   |
-| 0.012         | 2.0   | 3512 | 0.0777          | 0.9389    | 0.9524 | 0.9456 | 0.9871   |
-| 0.005         | 3.0   | 5268 | 0.0856          | 0.9386    | 0.9520 | 0.9453 | 0.9869   |
+| 0.0883        | 1.0   | 1756 | 0.0701          | 0.9168    | 0.9312 | 0.9239 | 0.9821   |
+| 0.0343        | 2.0   | 3512 | 0.0630          | 0.9329    | 0.9504 | 0.9416 | 0.9857   |
+| 0.0174        | 3.0   | 5268 | 0.0635          | 0.9379    | 0.9505 | 0.9442 | 0.9863   |
 
 
 ### Framework versions