End of training

README.md

@@ -20,21 +20,21 @@ model-index:
       name: ner
       type: ner
       config: indian_names
-      split: train
+      split: test
       args: indian_names
     metrics:
     - name: Precision
       type: precision
-      value: 0.9783236696036152
+      value: 0.9937446568944307
     - name: Recall
       type: recall
-      value: 0.9577025239110016
+      value: 0.9914087202529539
     - name: F1
       type: f1
-      value: 0.9679032760195776
+      value: 0.9925753142214493
     - name: Accuracy
       type: accuracy
-      value: 0.9800733834122645
+      value: 0.9952919271179678
 ---
 
 <!-- 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 [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ner dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1222
-- Precision: 0.9783
-- Recall: 0.9577
-- F1: 0.9679
-- Accuracy: 0.9801
+- Loss: 0.0119
+- Precision: 0.9937
+- Recall: 0.9914
+- F1: 0.9926
+- Accuracy: 0.9953
 
 ## 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   | 438  | 0.1036          | 0.9894    | 0.9413 | 0.9647 | 0.9785   |
-| 0.0608        | 2.0   | 876  | 0.1149          | 0.9875    | 0.9466 | 0.9666 | 0.9795   |
-| 0.0527        | 3.0   | 1314 | 0.1188          | 0.9846    | 0.9470 | 0.9654 | 0.9787   |
-| 0.0413        | 4.0   | 1752 | 0.1200          | 0.9840    | 0.9535 | 0.9685 | 0.9805   |
-| 0.0316        | 5.0   | 2190 | 0.1222          | 0.9783    | 0.9577 | 0.9679 | 0.9801   |
+| No log        | 1.0   | 438  | 0.0475          | 0.9884    | 0.9554 | 0.9716 | 0.9824   |
+| 0.0493        | 2.0   | 876  | 0.0342          | 0.9932    | 0.9647 | 0.9788 | 0.9868   |
+| 0.0449        | 3.0   | 1314 | 0.0238          | 0.9931    | 0.9758 | 0.9843 | 0.9902   |
+| 0.0319        | 4.0   | 1752 | 0.0152          | 0.9952    | 0.9855 | 0.9903 | 0.9939   |
+| 0.0224        | 5.0   | 2190 | 0.0119          | 0.9937    | 0.9914 | 0.9926 | 0.9953   |
 
 
 ### Framework versions