End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9906929945918752
+      value: 0.9939821779886587
     - name: Recall
       type: recall
-      value: 0.9895728643216081
+      value: 0.9958260869565217
     - name: F1
       type: f1
-      value: 0.9901326126579096
+      value: 0.9949032781188464
     - name: Accuracy
       type: accuracy
-      value: 0.9982668382668383
+      value: 0.999003984063745
 ---
 
 <!-- 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 indian_names dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0075
-- Precision: 0.9907
-- Recall: 0.9896
-- F1: 0.9901
-- Accuracy: 0.9983
+- Loss: 0.0050
+- Precision: 0.9940
+- Recall: 0.9958
+- F1: 0.9949
+- Accuracy: 0.9990
 
 ## Model description
 
@@ -79,11 +79,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 66   | 0.0313          | 0.9674    | 0.9595 | 0.9635 | 0.9926   |
-| No log        | 2.0   | 132  | 0.0185          | 0.9780    | 0.9732 | 0.9756 | 0.9951   |
-| No log        | 3.0   | 198  | 0.0120          | 0.9849    | 0.9830 | 0.9840 | 0.9970   |
-| No log        | 4.0   | 264  | 0.0090          | 0.9857    | 0.9849 | 0.9853 | 0.9975   |
-| No log        | 5.0   | 330  | 0.0075          | 0.9907    | 0.9896 | 0.9901 | 0.9983   |
+| No log        | 1.0   | 66   | 0.0440          | 0.9579    | 0.9650 | 0.9614 | 0.9906   |
+| No log        | 2.0   | 132  | 0.0191          | 0.9870    | 0.9821 | 0.9845 | 0.9959   |
+| No log        | 3.0   | 198  | 0.0098          | 0.9919    | 0.9899 | 0.9909 | 0.9980   |
+| No log        | 4.0   | 264  | 0.0061          | 0.9927    | 0.9935 | 0.9931 | 0.9987   |
+| No log        | 5.0   | 330  | 0.0050          | 0.9940    | 0.9958 | 0.9949 | 0.9990   |
 
 
 ### Framework versions