Training complete

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.932892561983471
+      value: 0.9332010582010583
     - name: Recall
       type: recall
       value: 0.9498485358465163
     - name: F1
       type: f1
-      value: 0.9412941961307538
+      value: 0.9414512093411176
     - name: Accuracy
       type: accuracy
-      value: 0.9859304173779949
+      value: 0.9862247601106728
 ---
 
 <!-- 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: 0.0647
-- Precision: 0.9329
+- Loss: 0.0631
+- Precision: 0.9332
 - Recall: 0.9498
-- F1: 0.9413
-- Accuracy: 0.9859
+- F1: 0.9415
+- Accuracy: 0.9862
 
 ## Model description
 
@@ -79,9 +79,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0739        | 1.0   | 1756 | 0.0738          | 0.9000    | 0.9320 | 0.9158 | 0.9799   |
-| 0.0348        | 2.0   | 3512 | 0.0715          | 0.9274    | 0.9416 | 0.9344 | 0.9836   |
-| 0.0219        | 3.0   | 5268 | 0.0647          | 0.9329    | 0.9498 | 0.9413 | 0.9859   |
+| 0.0761        | 1.0   | 1756 | 0.0599          | 0.9104    | 0.9391 | 0.9245 | 0.9834   |
+| 0.0339        | 2.0   | 3512 | 0.0661          | 0.9329    | 0.9470 | 0.9399 | 0.9854   |
+| 0.0231        | 3.0   | 5268 | 0.0631          | 0.9332    | 0.9498 | 0.9415 | 0.9862   |
 
 
 ### Framework versions