End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9905686167304538
+      value: 0.9906929945918752
     - name: Recall
       type: recall
-      value: 0.910427135678392
+      value: 0.9895728643216081
     - name: F1
       type: f1
-      value: 0.9488085886357684
+      value: 0.9901326126579096
     - name: Accuracy
       type: accuracy
-      value: 0.983080223080223
+      value: 0.9982668382668383
 ---
 
 <!-- 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: 0.0679
-- Precision: 0.9906
-- Recall: 0.9104
-- F1: 0.9488
-- Accuracy: 0.9831
+- Loss: 0.0075
+- Precision: 0.9907
+- Recall: 0.9896
+- F1: 0.9901
+- Accuracy: 0.9983
 
 ## 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   | 0.1499          | 0.7872    | 0.7281 | 0.7565 | 0.9557   |
-| No log        | 2.0   | 132  | 0.1338          | 0.8289    | 0.7524 | 0.7888 | 0.9612   |
-| No log        | 3.0   | 198  | 0.0884          | 0.9959    | 0.9053 | 0.9484 | 0.9820   |
-| No log        | 4.0   | 264  | 0.0750          | 0.9964    | 0.9070 | 0.9496 | 0.9826   |
-| No log        | 5.0   | 330  | 0.0679          | 0.9906    | 0.9104 | 0.9488 | 0.9831   |
+| 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   |
 
 
 ### Framework versions