anismahmahi
/

group2_non_all_zero

@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3907
-- Precision: 0.0415
-- Recall: 0.086
-- F1: 0.0560
-- Accuracy: 0.9044
 ## Model description
@@ -43,23 +43,32 @@ More information needed
 The following hyperparameters were used during training:
 - learning_rate: 3e-05
-- train_batch_size: 8
-- eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 6
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 172  | 0.2512          | 0.0608    | 0.044  | 0.0510 | 0.9439   |
-| No log        | 2.0   | 344  | 0.2667          | 0.0355    | 0.048  | 0.0408 | 0.9194   |
-| 0.4561        | 3.0   | 516  | 0.2883          | 0.0445    | 0.078  | 0.0566 | 0.9198   |
-| 0.4561        | 4.0   | 688  | 0.3610          | 0.0379    | 0.092  | 0.0537 | 0.8968   |
-| 0.4561        | 5.0   | 860  | 0.3840          | 0.0528    | 0.094  | 0.0676 | 0.9095   |
-| 0.1508        | 6.0   | 1032 | 0.3907          | 0.0415    | 0.086  | 0.0560 | 0.9044   |
 ### Framework versions

 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 2.3325
+- Precision: 0.0395
+- Recall: 0.182
+- F1: 0.0649
+- Accuracy: 0.8597
 ## Model description
 The following hyperparameters were used during training:
 - learning_rate: 3e-05
+- train_batch_size: 32
+- eval_batch_size: 32
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- num_epochs: 15
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 43   | 1.5592          | 0.0020    | 0.124  | 0.0040 | 0.3311   |
+| No log        | 2.0   | 86   | 1.2689          | 0.0104    | 0.14   | 0.0193 | 0.6247   |
+| No log        | 3.0   | 129  | 1.1742          | 0.0110    | 0.172  | 0.0206 | 0.6614   |
+| No log        | 4.0   | 172  | 1.3716          | 0.0147    | 0.178  | 0.0271 | 0.6468   |
+| No log        | 5.0   | 215  | 1.3265          | 0.0177    | 0.178  | 0.0323 | 0.7203   |
+| No log        | 6.0   | 258  | 1.5835          | 0.0217    | 0.176  | 0.0386 | 0.7574   |
+| No log        | 7.0   | 301  | 1.6678          | 0.0249    | 0.174  | 0.0435 | 0.7952   |
+| No log        | 8.0   | 344  | 1.9432          | 0.0387    | 0.18   | 0.0636 | 0.8551   |
+| No log        | 9.0   | 387  | 1.9371          | 0.0306    | 0.188  | 0.0526 | 0.7962   |
+| No log        | 10.0  | 430  | 2.0129          | 0.0305    | 0.182  | 0.0523 | 0.8187   |
+| No log        | 11.0  | 473  | 2.1952          | 0.0402    | 0.192  | 0.0664 | 0.8595   |
+| 0.5993        | 12.0  | 516  | 2.1873          | 0.0369    | 0.182  | 0.0614 | 0.8512   |
+| 0.5993        | 13.0  | 559  | 2.2653          | 0.0394    | 0.18   | 0.0646 | 0.8583   |
+| 0.5993        | 14.0  | 602  | 2.3001          | 0.0397    | 0.184  | 0.0653 | 0.8553   |
+| 0.5993        | 15.0  | 645  | 2.3325          | 0.0395    | 0.182  | 0.0649 | 0.8597   |
 ### Framework versions