update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9306692773228907
+      value: 0.9267369114257491
     - name: Recall
       type: recall
-      value: 0.9381841019199713
+      value: 0.9473241332884551
     - name: F1
       type: f1
-      value: 0.9344115807345187
+      value: 0.9369174434087884
     - name: Accuracy
       type: accuracy
-      value: 0.9832666156472597
+      value: 0.9852239948195679
 ---
 
 <!-- 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: 0.1183
-- Precision: 0.9307
-- Recall: 0.9382
-- F1: 0.9344
-- Accuracy: 0.9833
+- Loss: 0.1060
+- Precision: 0.9267
+- Recall: 0.9473
+- F1: 0.9369
+- Accuracy: 0.9852
 
 ## Model description
 
@@ -79,16 +79,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1081        | 1.0   | 1756  | 0.0963          | 0.8947    | 0.8982 | 0.8964 | 0.9742   |
-| 0.0518        | 2.0   | 3512  | 0.0780          | 0.9219    | 0.9182 | 0.9200 | 0.9803   |
-| 0.0348        | 3.0   | 5268  | 0.0833          | 0.9258    | 0.9271 | 0.9264 | 0.9819   |
-| 0.0268        | 4.0   | 7024  | 0.0900          | 0.9152    | 0.9241 | 0.9196 | 0.9805   |
-| 0.0167        | 5.0   | 8780  | 0.0929          | 0.9225    | 0.9320 | 0.9272 | 0.9822   |
-| 0.0071        | 6.0   | 10536 | 0.1119          | 0.9229    | 0.9270 | 0.9249 | 0.9816   |
-| 0.0056        | 7.0   | 12292 | 0.1073          | 0.9286    | 0.9366 | 0.9326 | 0.9832   |
-| 0.0021        | 8.0   | 14048 | 0.1194          | 0.9285    | 0.9350 | 0.9318 | 0.9829   |
-| 0.0019        | 9.0   | 15804 | 0.1156          | 0.9318    | 0.9376 | 0.9347 | 0.9833   |
-| 0.0011        | 10.0  | 17560 | 0.1183          | 0.9307    | 0.9382 | 0.9344 | 0.9833   |
+| 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   |
 
 
 ### Framework versions