End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9994470908472269
+      value: 1.0
     - name: Recall
       type: recall
-      value: 0.9994045846978268
+      value: 0.999957470335559
     - name: F1
       type: f1
-      value: 0.9994258373205741
+      value: 0.9999787347155767
     - name: Accuracy
       type: accuracy
-      value: 0.9998240191819092
+      value: 0.9999890011988694
 ---
 
 <!-- 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: 0.0015
-- Precision: 0.9994
-- Recall: 0.9994
-- F1: 0.9994
-- Accuracy: 0.9998
+- Loss: 0.0000
+- Precision: 1.0
+- Recall: 1.0000
+- F1: 1.0000
+- Accuracy: 1.0000
 
 ## Model description
 
@@ -73,17 +73,22 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 5
+- num_epochs: 10
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1477        | 1.0   | 626  | 0.0548          | 0.9686    | 0.9604 | 0.9644 | 0.9887   |
-| 0.0571        | 2.0   | 1252 | 0.0249          | 0.9833    | 0.9820 | 0.9827 | 0.9949   |
-| 0.037         | 3.0   | 1878 | 0.0075          | 0.9962    | 0.9953 | 0.9957 | 0.9987   |
-| 0.0101        | 4.0   | 2504 | 0.0027          | 0.9987    | 0.9984 | 0.9986 | 0.9996   |
-| 0.004         | 5.0   | 3130 | 0.0015          | 0.9994    | 0.9994 | 0.9994 | 0.9998   |
+| 0.0127        | 1.0   | 626  | 0.0069          | 0.9955    | 0.9957 | 0.9956 | 0.9986   |
+| 0.01          | 2.0   | 1252 | 0.0068          | 0.9972    | 0.9971 | 0.9972 | 0.9991   |
+| 0.0075        | 3.0   | 1878 | 0.0029          | 0.9987    | 0.9982 | 0.9984 | 0.9995   |
+| 0.006         | 4.0   | 2504 | 0.0010          | 0.9994    | 0.9994 | 0.9994 | 0.9998   |
+| 0.0052        | 5.0   | 3130 | 0.0007          | 0.9997    | 0.9997 | 0.9997 | 0.9999   |
+| 0.0032        | 6.0   | 3756 | 0.0003          | 0.9999    | 0.9998 | 0.9999 | 1.0000   |
+| 0.003         | 7.0   | 4382 | 0.0001          | 1.0000    | 1.0000 | 1.0000 | 1.0000   |
+| 0.0013        | 8.0   | 5008 | 0.0000          | 1.0       | 1.0    | 1.0    | 1.0      |
+| 0.0013        | 9.0   | 5634 | 0.0001          | 1.0000    | 0.9999 | 0.9999 | 1.0000   |
+| 0.0011        | 10.0  | 6260 | 0.0000          | 1.0       | 1.0000 | 1.0000 | 1.0000   |
 
 
 ### Framework versions