End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9814334577809573
+      value: 0.9896954662296407
     - name: Recall
       type: recall
-      value: 0.9663647269885645
+      value: 0.9704150478224023
     - name: F1
       type: f1
-      value: 0.9738408043522868
+      value: 0.9799604321344418
     - name: Accuracy
       type: accuracy
-      value: 0.9864516687615129
+      value: 0.9894401834309103
 ---
 
 <!-- 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: 0.0429
-- Precision: 0.9814
-- Recall: 0.9664
-- F1: 0.9738
-- Accuracy: 0.9865
+- Loss: 0.0320
+- Precision: 0.9897
+- Recall: 0.9704
+- F1: 0.9800
+- Accuracy: 0.9894
 
 ## Model description
 
@@ -67,7 +67,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 4e-05
+- learning_rate: 0.0001
 - train_batch_size: 16
 - eval_batch_size: 16
 - seed: 42
@@ -79,11 +79,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0981        | 0.58  | 500  | 0.0546          | 0.9699    | 0.9642 | 0.9670 | 0.9829   |
-| 0.0528        | 1.17  | 1000 | 0.0487          | 0.9763    | 0.9649 | 0.9706 | 0.9848   |
-| 0.0485        | 1.75  | 1500 | 0.0462          | 0.9796    | 0.9643 | 0.9719 | 0.9855   |
-| 0.0439        | 2.33  | 2000 | 0.0447          | 0.9795    | 0.9662 | 0.9728 | 0.9859   |
-| 0.0426        | 2.91  | 2500 | 0.0429          | 0.9814    | 0.9664 | 0.9738 | 0.9865   |
+| 0.0503        | 0.58  | 500  | 0.0506          | 0.9744    | 0.9656 | 0.9700 | 0.9846   |
+| 0.0461        | 1.17  | 1000 | 0.0450          | 0.9781    | 0.9657 | 0.9719 | 0.9856   |
+| 0.0428        | 1.75  | 1500 | 0.0424          | 0.9804    | 0.9677 | 0.9740 | 0.9864   |
+| 0.0379        | 2.33  | 2000 | 0.0375          | 0.9839    | 0.9704 | 0.9771 | 0.9880   |
+| 0.0352        | 2.91  | 2500 | 0.0320          | 0.9897    | 0.9704 | 0.9800 | 0.9894   |
 
 
 ### Framework versions