End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9986185030007927
+      value: 0.9805654698636115
     - name: Recall
       type: recall
-      value: 0.9989804934411745
+      value: 0.9645927116237604
     - name: F1
       type: f1
-      value: 0.998799465422339
+      value: 0.9725135102144988
     - name: Accuracy
       type: accuracy
-      value: 0.9994169549500524
+      value: 0.9858187365837784
 ---
 
 <!-- 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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0020
-- Precision: 0.9986
-- Recall: 0.9990
-- F1: 0.9988
-- Accuracy: 0.9994
+- Loss: 0.0454
+- Precision: 0.9806
+- Recall: 0.9646
+- F1: 0.9725
+- Accuracy: 0.9858
 
 ## Model description
 
@@ -79,8 +79,8 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0068        | 1.03  | 500  | 0.0054          | 0.9976    | 0.9944 | 0.9960 | 0.9980   |
-| 0.0076        | 2.06  | 1000 | 0.0020          | 0.9986    | 0.9990 | 0.9988 | 0.9994   |
+| 0.0831        | 1.17  | 500  | 0.0507          | 0.9756    | 0.9645 | 0.9700 | 0.9845   |
+| 0.0473        | 2.33  | 1000 | 0.0454          | 0.9806    | 0.9646 | 0.9725 | 0.9858   |
 
 
 ### Framework versions