update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9267369114257491
+      value: 0.932077342588002
     - name: Recall
       type: recall
-      value: 0.9473241332884551
+      value: 0.9491753618310333
     - name: F1
       type: f1
-      value: 0.9369174434087884
+      value: 0.940548653381139
     - name: Accuracy
       type: accuracy
-      value: 0.9852239948195679
+      value: 0.984782480720551
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1060
-- Precision: 0.9267
-- Recall: 0.9473
-- F1: 0.9369
-- Accuracy: 0.9852
+- Loss: 0.1088
+- Precision: 0.9321
+- Recall: 0.9492
+- F1: 0.9405
+- Accuracy: 0.9848
 
 ## Model description
 
@@ -79,16 +79,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1012        | 1.0   | 1756  | 0.0895          | 0.8924    | 0.9194 | 0.9057 | 0.9767   |
-| 0.0491        | 2.0   | 3512  | 0.0818          | 0.9070    | 0.9260 | 0.9164 | 0.9801   |
-| 0.0334        | 3.0   | 5268  | 0.0818          | 0.9170    | 0.9315 | 0.9242 | 0.9821   |
-| 0.0235        | 4.0   | 7024  | 0.0893          | 0.9074    | 0.9364 | 0.9216 | 0.9815   |
-| 0.0167        | 5.0   | 8780  | 0.0879          | 0.9106    | 0.9414 | 0.9258 | 0.9828   |
-| 0.0071        | 6.0   | 10536 | 0.0955          | 0.9172    | 0.9435 | 0.9301 | 0.9836   |
-| 0.0039        | 7.0   | 12292 | 0.1016          | 0.9209    | 0.9423 | 0.9315 | 0.9835   |
-| 0.0021        | 8.0   | 14048 | 0.1043          | 0.9294    | 0.9463 | 0.9378 | 0.9847   |
-| 0.0014        | 9.0   | 15804 | 0.1064          | 0.9271    | 0.9475 | 0.9372 | 0.9853   |
-| 0.0005        | 10.0  | 17560 | 0.1060          | 0.9267    | 0.9473 | 0.9369 | 0.9852   |
+| 0.1015        | 1.0   | 1756  | 0.1001          | 0.8858    | 0.9167 | 0.9010 | 0.9740   |
+| 0.049         | 2.0   | 3512  | 0.0803          | 0.8993    | 0.9273 | 0.9131 | 0.9798   |
+| 0.0327        | 3.0   | 5268  | 0.0794          | 0.9199    | 0.9350 | 0.9274 | 0.9821   |
+| 0.0237        | 4.0   | 7024  | 0.0880          | 0.9050    | 0.9344 | 0.9194 | 0.9813   |
+| 0.0131        | 5.0   | 8780  | 0.0849          | 0.9178    | 0.9446 | 0.9310 | 0.9837   |
+| 0.0073        | 6.0   | 10536 | 0.0975          | 0.9166    | 0.9446 | 0.9304 | 0.9838   |
+| 0.0044        | 7.0   | 12292 | 0.0965          | 0.9267    | 0.9475 | 0.9370 | 0.9842   |
+| 0.0015        | 8.0   | 14048 | 0.1075          | 0.9273    | 0.9463 | 0.9367 | 0.9843   |
+| 0.0011        | 9.0   | 15804 | 0.1089          | 0.9317    | 0.9480 | 0.9398 | 0.9847   |
+| 0.0006        | 10.0  | 17560 | 0.1088          | 0.9321    | 0.9492 | 0.9405 | 0.9848   |
 
 
 ### Framework versions