LovenOO
/

distilBERT_without_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: 0.7927
-- Precision: 0.8512
-- Recall: 0.8478
-- F1: 0.8484
-- Accuracy: 0.8842
 ## Model description
@@ -43,7 +43,7 @@ More information needed
 ### Training hyperparameters
 The following hyperparameters were used during training:
-- learning_rate: 5e-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   | 257  | 0.5177          | 0.7547    | 0.8422 | 0.7806 | 0.8444   |
-| 0.6866        | 2.0   | 514  | 0.4727          | 0.8301    | 0.8543 | 0.8372 | 0.8794   |
-| 0.6866        | 3.0   | 771  | 0.5257          | 0.8261    | 0.8508 | 0.8347 | 0.8779   |
-| 0.2332        | 4.0   | 1028 | 0.5768          | 0.8254    | 0.8651 | 0.8423 | 0.8818   |
-| 0.2332        | 5.0   | 1285 | 0.6244          | 0.8405    | 0.8529 | 0.8462 | 0.8852   |
-| 0.1201        | 6.0   | 1542 | 0.7367          | 0.8520    | 0.8507 | 0.8505 | 0.8838   |
-| 0.1201        | 7.0   | 1799 | 0.6644          | 0.8419    | 0.8607 | 0.8498 | 0.8833   |
-| 0.0848        | 8.0   | 2056 | 0.7632          | 0.8522    | 0.8433 | 0.8465 | 0.8833   |
-| 0.0848        | 9.0   | 2313 | 0.7510          | 0.8515    | 0.8569 | 0.8532 | 0.8867   |
-| 0.0517        | 10.0  | 2570 | 0.7927          | 0.8512    | 0.8478 | 0.8484 | 0.8842   |
 ### 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: 0.7575
+- Precision: 0.8533
+- Recall: 0.8477
+- F1: 0.8486
+- Accuracy: 0.8847
 ## Model description
 ### Training hyperparameters
 The following hyperparameters were used during training:
+- learning_rate: 3e-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   | 257  | 0.5635          | 0.7528    | 0.8373 | 0.7788 | 0.8439   |
+| 0.7607        | 2.0   | 514  | 0.5324          | 0.8060    | 0.8314 | 0.8098 | 0.8648   |
+| 0.7607        | 3.0   | 771  | 0.5216          | 0.8152    | 0.8475 | 0.8265 | 0.8765   |
+| 0.2593        | 4.0   | 1028 | 0.5493          | 0.8179    | 0.8585 | 0.8348 | 0.8823   |
+| 0.2593        | 5.0   | 1285 | 0.6226          | 0.8220    | 0.8419 | 0.8308 | 0.8794   |
+| 0.1473        | 6.0   | 1542 | 0.6677          | 0.8429    | 0.8485 | 0.8442 | 0.8818   |
+| 0.1473        | 7.0   | 1799 | 0.6611          | 0.8316    | 0.8481 | 0.8381 | 0.8823   |
+| 0.096         | 8.0   | 2056 | 0.7404          | 0.8528    | 0.8448 | 0.8478 | 0.8857   |
+| 0.096         | 9.0   | 2313 | 0.7401          | 0.8531    | 0.8476 | 0.8484 | 0.8862   |
+| 0.0642        | 10.0  | 2570 | 0.7575          | 0.8533    | 0.8477 | 0.8486 | 0.8847   |
 ### Framework versions