update model card README.md

README.md

@@ -22,16 +22,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.012011744817154552
+      value: 0.9327342290239345
     - name: Recall
       type: recall
-      value: 0.012111968419163826
+      value: 0.9405167773192177
     - name: F1
       type: f1
-      value: 0.012061648425284788
+      value: 0.9366093366093367
     - name: Accuracy
       type: accuracy
-      value: 0.76080237828928
+      value: 0.9850621063165951
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -42,10 +42,10 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0723
-- Precision: 0.0120
-- Recall: 0.0121
-- F1: 0.0121
-- Accuracy: 0.7608
+- Precision: 0.9327
+- Recall: 0.9405
+- F1: 0.9366
+- Accuracy: 0.9851
 
 ## Model description
 
@@ -76,9 +76,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 220  | 0.0754          | 0.0118    | 0.0118 | 0.0118 | 0.7605   |
-| No log        | 2.0   | 440  | 0.0688          | 0.0125    | 0.0127 | 0.0126 | 0.7608   |
-| 0.0717        | 3.0   | 660  | 0.0723          | 0.0120    | 0.0121 | 0.0121 | 0.7608   |
+| No log        | 1.0   | 220  | 0.0754          | 0.9225    | 0.9296 | 0.9260 | 0.9831   |
+| No log        | 2.0   | 440  | 0.0688          | 0.9319    | 0.9407 | 0.9363 | 0.9849   |
+| 0.0717        | 3.0   | 660  | 0.0723          | 0.9327    | 0.9405 | 0.9366 | 0.9851   |
 
 
 ### Framework versions