End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9941348973607038
+      value: 0.7241379310344828
     - name: Recall
       type: recall
-      value: 0.9921951219512195
+      value: 0.7170731707317073
     - name: F1
       type: f1
-      value: 0.9931640625
+      value: 0.7205882352941175
     - name: Accuracy
       type: accuracy
-      value: 0.9998052125131481
+      value: 0.9920721492851299
 ---
 
 <!-- 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: 0.0012
-- Precision: 0.9941
-- Recall: 0.9922
-- F1: 0.9932
-- Accuracy: 0.9998
+- Loss: 0.0278
+- Precision: 0.7241
+- Recall: 0.7171
+- F1: 0.7206
+- Accuracy: 0.9921
 
 ## Model description
 
@@ -73,27 +73,17 @@ 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: 15
+- num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 63   | 0.1413          | 0.0       | 0.0    | 0.0    | 0.9745   |
-| No log        | 2.0   | 126  | 0.1211          | 0.0       | 0.0    | 0.0    | 0.9745   |
-| No log        | 3.0   | 189  | 0.0656          | 0.6231    | 0.2790 | 0.3854 | 0.9811   |
-| No log        | 4.0   | 252  | 0.0380          | 0.7297    | 0.6059 | 0.6620 | 0.9894   |
-| No log        | 5.0   | 315  | 0.0259          | 0.8341    | 0.7259 | 0.7762 | 0.9931   |
-| No log        | 6.0   | 378  | 0.0136          | 0.8842    | 0.8712 | 0.8776 | 0.9963   |
-| No log        | 7.0   | 441  | 0.0076          | 0.9286    | 0.9268 | 0.9277 | 0.9981   |
-| 0.0748        | 8.0   | 504  | 0.0054          | 0.9409    | 0.9473 | 0.9441 | 0.9985   |
-| 0.0748        | 9.0   | 567  | 0.0042          | 0.9520    | 0.9678 | 0.9598 | 0.9991   |
-| 0.0748        | 10.0  | 630  | 0.0025          | 0.9738    | 0.9795 | 0.9767 | 0.9995   |
-| 0.0748        | 11.0  | 693  | 0.0019          | 0.9863    | 0.9863 | 0.9863 | 0.9997   |
-| 0.0748        | 12.0  | 756  | 0.0015          | 0.9961    | 0.9912 | 0.9936 | 0.9998   |
-| 0.0748        | 13.0  | 819  | 0.0014          | 0.9912    | 0.9912 | 0.9912 | 0.9998   |
-| 0.0748        | 14.0  | 882  | 0.0013          | 0.9912    | 0.9912 | 0.9912 | 0.9998   |
-| 0.0748        | 15.0  | 945  | 0.0012          | 0.9941    | 0.9922 | 0.9932 | 0.9998   |
+| No log        | 1.0   | 63   | 0.1197          | 0.0       | 0.0    | 0.0    | 0.9745   |
+| No log        | 2.0   | 126  | 0.0818          | 0.8438    | 0.0263 | 0.0511 | 0.9751   |
+| No log        | 3.0   | 189  | 0.0470          | 0.5270    | 0.4859 | 0.5056 | 0.9857   |
+| No log        | 4.0   | 252  | 0.0315          | 0.7042    | 0.6341 | 0.6674 | 0.9906   |
+| No log        | 5.0   | 315  | 0.0278          | 0.7241    | 0.7171 | 0.7206 | 0.9921   |
 
 
 ### Framework versions