End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.5583832335329342
+      value: 0.9805194805194806
     - name: Recall
       type: recall
-      value: 0.520949720670391
+      value: 0.984171322160149
     - name: F1
       type: f1
-      value: 0.5390173410404625
+      value: 0.9823420074349444
     - name: Accuracy
       type: accuracy
-      value: 0.9721157973387596
+      value: 0.9989348679713209
 ---
 
 <!-- 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: 0.1028
-- Precision: 0.5584
-- Recall: 0.5209
-- F1: 0.5390
-- Accuracy: 0.9721
+- Loss: 0.0053
+- Precision: 0.9805
+- Recall: 0.9842
+- F1: 0.9823
+- Accuracy: 0.9989
 
 ## Model description
 
@@ -67,20 +67,23 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2e-05
+- learning_rate: 5e-05
 - train_batch_size: 16
 - eval_batch_size: 16
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 2
+- num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 213  | 0.1348          | 0.4605    | 0.3613 | 0.4049 | 0.9633   |
-| No log        | 2.0   | 426  | 0.1028          | 0.5584    | 0.5209 | 0.5390 | 0.9721   |
+| No log        | 1.0   | 213  | 0.0572          | 0.6793    | 0.7356 | 0.7063 | 0.9820   |
+| No log        | 2.0   | 426  | 0.0248          | 0.8912    | 0.8887 | 0.8900 | 0.9936   |
+| 0.0713        | 3.0   | 639  | 0.0118          | 0.9570    | 0.9534 | 0.9552 | 0.9973   |
+| 0.0713        | 4.0   | 852  | 0.0067          | 0.9777    | 0.9800 | 0.9788 | 0.9987   |
+| 0.0164        | 5.0   | 1065 | 0.0053          | 0.9805    | 0.9842 | 0.9823 | 0.9989   |
 
 
 ### Framework versions