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: 0.6943
-- Precision: 0.8467
-- Recall: 0.8562
-- F1: 0.8509
-- Accuracy: 0.8793
 ## 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   | 257  | 0.5790          | 0.7659    | 0.8225 | 0.7875 | 0.8472   |
-| 0.7473        | 2.0   | 514  | 0.5007          | 0.8115    | 0.8503 | 0.8264 | 0.8647   |
-| 0.7473        | 3.0   | 771  | 0.4903          | 0.8007    | 0.8418 | 0.8174 | 0.8594   |
-| 0.2608        | 4.0   | 1028 | 0.5370          | 0.8249    | 0.8491 | 0.8350 | 0.8657   |
-| 0.2608        | 5.0   | 1285 | 0.6034          | 0.8424    | 0.8514 | 0.8455 | 0.8803   |
-| 0.1543        | 6.0   | 1542 | 0.5988          | 0.8396    | 0.8565 | 0.8466 | 0.8788   |
-| 0.1543        | 7.0   | 1799 | 0.6736          | 0.8486    | 0.8453 | 0.8458 | 0.8769   |
-| 0.0981        | 8.0   | 2056 | 0.6476          | 0.8400    | 0.8605 | 0.8492 | 0.8788   |
-| 0.0981        | 9.0   | 2313 | 0.6837          | 0.8443    | 0.8510 | 0.8469 | 0.8788   |
-| 0.0713        | 10.0  | 2570 | 0.6943          | 0.8467    | 0.8562 | 0.8509 | 0.8793   |
 ### 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: 0.6731
+- Precision: 0.8400
+- Recall: 0.8427
+- F1: 0.8407
+- Accuracy: 0.8779
 ## 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   | 257  | 0.6542          | 0.7446    | 0.8052 | 0.7657 | 0.8350   |
+| 0.8635        | 2.0   | 514  | 0.5548          | 0.7961    | 0.8277 | 0.8056 | 0.8540   |
+| 0.8635        | 3.0   | 771  | 0.4839          | 0.7912    | 0.8427 | 0.8115 | 0.8589   |
+| 0.3097        | 4.0   | 1028 | 0.5256          | 0.8148    | 0.8544 | 0.8315 | 0.8667   |
+| 0.3097        | 5.0   | 1285 | 0.5657          | 0.8346    | 0.8494 | 0.8413 | 0.8764   |
+| 0.1839        | 6.0   | 1542 | 0.6005          | 0.8208    | 0.8430 | 0.8304 | 0.8710   |
+| 0.1839        | 7.0   | 1799 | 0.6580          | 0.8319    | 0.8349 | 0.8314 | 0.8706   |
+| 0.1254        | 8.0   | 2056 | 0.6348          | 0.8342    | 0.8515 | 0.8423 | 0.8774   |
+| 0.1254        | 9.0   | 2313 | 0.6601          | 0.8314    | 0.8394 | 0.8348 | 0.8745   |
+| 0.0935        | 10.0  | 2570 | 0.6731          | 0.8400    | 0.8427 | 0.8407 | 0.8779   |
 ### Framework versions