Training complete

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9341604631927213
+      value: 0.9342388603611065
     - name: Recall
       type: recall
-      value: 0.9503534163581285
+      value: 0.9491753618310333
     - name: F1
       type: f1
-      value: 0.9421873696504547
+      value: 0.9416478837966441
     - name: Accuracy
       type: accuracy
-      value: 0.9865191028433508
+      value: 0.9862689115205746
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ 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: 0.0615
+- Loss: 0.0636
 - Precision: 0.9342
-- Recall: 0.9504
-- F1: 0.9422
-- Accuracy: 0.9865
+- Recall: 0.9492
+- F1: 0.9416
+- Accuracy: 0.9863
 
 ## Model description
 
@@ -79,9 +79,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0765        | 1.0   | 1756 | 0.0651          | 0.9007    | 0.9329 | 0.9165 | 0.9810   |
-| 0.0373        | 2.0   | 3512 | 0.0626          | 0.9324    | 0.9451 | 0.9387 | 0.9853   |
-| 0.0228        | 3.0   | 5268 | 0.0615          | 0.9342    | 0.9504 | 0.9422 | 0.9865   |
+| 0.0759        | 1.0   | 1756 | 0.0645          | 0.9062    | 0.9337 | 0.9198 | 0.9813   |
+| 0.0371        | 2.0   | 3512 | 0.0652          | 0.9327    | 0.9473 | 0.9400 | 0.9857   |
+| 0.0229        | 3.0   | 5268 | 0.0636          | 0.9342    | 0.9492 | 0.9416 | 0.9863   |
 
 
 ### Framework versions