kSaluja
/

new-test-model2

@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1087
-- Precision: 0.9702
-- Recall: 0.9750
-- F1: 0.9726
-- Accuracy: 0.9794
 ## Model description
@@ -54,21 +54,21 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 151  | 0.1812          | 0.9358    | 0.9407 | 0.9382 | 0.9537   |
-| No log        | 2.0   | 302  | 0.1436          | 0.9501    | 0.9602 | 0.9551 | 0.9636   |
-| No log        | 3.0   | 453  | 0.1088          | 0.9638    | 0.9693 | 0.9666 | 0.9735   |
-| 0.3121        | 4.0   | 604  | 0.1424          | 0.9565    | 0.9648 | 0.9606 | 0.9700   |
-| 0.3121        | 5.0   | 755  | 0.1089          | 0.9686    | 0.9659 | 0.9672 | 0.9765   |
-| 0.3121        | 6.0   | 906  | 0.0960          | 0.9662    | 0.9741 | 0.9701 | 0.9786   |
-| 0.0384        | 7.0   | 1057 | 0.1049          | 0.9723    | 0.9746 | 0.9734 | 0.9793   |
-| 0.0384        | 8.0   | 1208 | 0.1059          | 0.9708    | 0.9746 | 0.9727 | 0.9798   |
-| 0.0384        | 9.0   | 1359 | 0.1094          | 0.9693    | 0.9752 | 0.9723 | 0.9791   |
-| 0.0137        | 10.0  | 1510 | 0.1087          | 0.9702    | 0.9750 | 0.9726 | 0.9794   |
 ### Framework versions
 - Transformers 4.18.0
-- Pytorch 1.10.0+cu111
 - Datasets 2.1.0
 - Tokenizers 0.12.1

 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.1040
+- Precision: 0.9722
+- Recall: 0.9757
+- F1: 0.9739
+- Accuracy: 0.9808
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 151  | 0.1819          | 0.9360    | 0.9405 | 0.9382 | 0.9540   |
+| No log        | 2.0   | 302  | 0.1196          | 0.9637    | 0.9639 | 0.9638 | 0.9703   |
+| No log        | 3.0   | 453  | 0.1322          | 0.9614    | 0.9682 | 0.9648 | 0.9711   |
+| 0.2764        | 4.0   | 604  | 0.1071          | 0.9677    | 0.9725 | 0.9701 | 0.9763   |
+| 0.2764        | 5.0   | 755  | 0.1084          | 0.9709    | 0.9766 | 0.9737 | 0.9790   |
+| 0.2764        | 6.0   | 906  | 0.1015          | 0.9717    | 0.9739 | 0.9728 | 0.9791   |
+| 0.0342        | 7.0   | 1057 | 0.1208          | 0.9686    | 0.9727 | 0.9706 | 0.9785   |
+| 0.0342        | 8.0   | 1208 | 0.1068          | 0.9680    | 0.9752 | 0.9716 | 0.9798   |
+| 0.0342        | 9.0   | 1359 | 0.1028          | 0.9719    | 0.9743 | 0.9731 | 0.9807   |
+| 0.0129        | 10.0  | 1510 | 0.1040          | 0.9722    | 0.9757 | 0.9739 | 0.9808   |
 ### Framework versions
 - Transformers 4.18.0
+- Pytorch 1.11.0+cu113
 - Datasets 2.1.0
 - Tokenizers 0.12.1