LovenOO
/

distilBERT_mergeddata_with_preprocessing_grid_search

@@ -20,11 +20,11 @@ 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 None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1825
-- Precision: 0.9668
-- Recall: 0.9672
-- F1: 0.9669
-- Accuracy: 0.9672
 ## Model description
@@ -43,7 +43,7 @@ More information needed
 ### Training hyperparameters
 The following hyperparameters were used during training:
-- learning_rate: 3e-05
 - train_batch_size: 32
 - eval_batch_size: 32
 - seed: 42
@@ -55,16 +55,16 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 225  | 0.1820          | 0.9527    | 0.9500 | 0.9506 | 0.9511   |
-| No log        | 2.0   | 450  | 0.1582          | 0.9583    | 0.9584 | 0.9578 | 0.9583   |
-| 0.3586        | 3.0   | 675  | 0.1369          | 0.9677    | 0.9678 | 0.9676 | 0.9678   |
-| 0.3586        | 4.0   | 900  | 0.1371          | 0.9702    | 0.9706 | 0.9703 | 0.9706   |
-| 0.0493        | 5.0   | 1125 | 0.1567          | 0.9686    | 0.9690 | 0.9687 | 0.9689   |
-| 0.0493        | 6.0   | 1350 | 0.1622          | 0.9680    | 0.9685 | 0.9681 | 0.9683   |
-| 0.0181        | 7.0   | 1575 | 0.1684          | 0.9640    | 0.9643 | 0.9640 | 0.9644   |
-| 0.0181        | 8.0   | 1800 | 0.1717          | 0.9663    | 0.9666 | 0.9664 | 0.9667   |
-| 0.0051        | 9.0   | 2025 | 0.1791          | 0.9674    | 0.9678 | 0.9675 | 0.9678   |
-| 0.0051        | 10.0  | 2250 | 0.1825          | 0.9668    | 0.9672 | 0.9669 | 0.9672   |
 ### Framework versions

 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.1742
+- Precision: 0.9650
+- Recall: 0.9650
+- F1: 0.9648
+- Accuracy: 0.965
 ## Model description
 ### Training hyperparameters
 The following hyperparameters were used during training:
+- learning_rate: 2e-05
 - train_batch_size: 32
 - eval_batch_size: 32
 - seed: 42
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 225  | 0.2068          | 0.9550    | 0.9536 | 0.9537 | 0.9544   |
+| No log        | 2.0   | 450  | 0.1497          | 0.9583    | 0.9585 | 0.9582 | 0.9583   |
+| 0.445         | 3.0   | 675  | 0.1408          | 0.9628    | 0.9631 | 0.9627 | 0.9628   |
+| 0.445         | 4.0   | 900  | 0.1484          | 0.9630    | 0.9630 | 0.9626 | 0.9628   |
+| 0.0585        | 5.0   | 1125 | 0.1487          | 0.9675    | 0.9680 | 0.9676 | 0.9678   |
+| 0.0585        | 6.0   | 1350 | 0.1538          | 0.9665    | 0.9670 | 0.9665 | 0.9667   |
+| 0.0242        | 7.0   | 1575 | 0.1666          | 0.9644    | 0.9645 | 0.9642 | 0.9644   |
+| 0.0242        | 8.0   | 1800 | 0.1709          | 0.9672    | 0.9673 | 0.9671 | 0.9672   |
+| 0.0111        | 9.0   | 2025 | 0.1707          | 0.9670    | 0.9672 | 0.9670 | 0.9672   |
+| 0.0111        | 10.0  | 2250 | 0.1742          | 0.9650    | 0.9650 | 0.9648 | 0.965    |
 ### Framework versions