LovenOO
/

BERT_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 [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.9805
-- Precision: 0.8330
-- Recall: 0.8192
-- F1: 0.8255
-- Accuracy: 0.8630
 ## 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: 16
 - eval_batch_size: 16
 - seed: 42
@@ -55,16 +55,16 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.9875        | 1.0   | 510  | 0.5592          | 0.7906    | 0.8234 | 0.8042 | 0.8463   |
-| 0.4428        | 2.0   | 1020 | 0.6291          | 0.8177    | 0.8243 | 0.8198 | 0.8606   |
-| 0.2998        | 3.0   | 1530 | 0.6034          | 0.8133    | 0.8342 | 0.8209 | 0.8586   |
-| 0.2088        | 4.0   | 2040 | 0.7925          | 0.8365    | 0.8152 | 0.8231 | 0.8562   |
-| 0.1563        | 5.0   | 2550 | 0.7368          | 0.8096    | 0.8301 | 0.8190 | 0.8571   |
-| 0.1302        | 6.0   | 3060 | 0.9748          | 0.8449    | 0.8036 | 0.8208 | 0.8591   |
-| 0.1024        | 7.0   | 3570 | 0.9088          | 0.8364    | 0.8264 | 0.8300 | 0.8645   |
-| 0.0897        | 8.0   | 4080 | 0.9440          | 0.8340    | 0.8254 | 0.8283 | 0.8635   |
-| 0.067         | 9.0   | 4590 | 0.9582          | 0.8370    | 0.8169 | 0.8260 | 0.8635   |
-| 0.0526        | 10.0  | 5100 | 0.9805          | 0.8330    | 0.8192 | 0.8255 | 0.8630   |
 ### Framework versions

 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.9426
+- Precision: 0.8396
+- Recall: 0.8182
+- F1: 0.8282
+- Accuracy: 0.8655
 ## Model description
 ### Training hyperparameters
 The following hyperparameters were used during training:
+- learning_rate: 3e-05
 - train_batch_size: 16
 - eval_batch_size: 16
 - seed: 42
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.9585        | 1.0   | 510  | 0.5849          | 0.7825    | 0.8293 | 0.8002 | 0.8473   |
+| 0.4334        | 2.0   | 1020 | 0.6323          | 0.8394    | 0.8127 | 0.8226 | 0.8625   |
+| 0.281         | 3.0   | 1530 | 0.5389          | 0.8259    | 0.8476 | 0.8348 | 0.8704   |
+| 0.2117        | 4.0   | 2040 | 0.7155          | 0.8381    | 0.8243 | 0.8297 | 0.8675   |
+| 0.1556        | 5.0   | 2550 | 0.6981          | 0.8420    | 0.8411 | 0.8414 | 0.8729   |
+| 0.1216        | 6.0   | 3060 | 0.9238          | 0.8441    | 0.8089 | 0.8237 | 0.8606   |
+| 0.108         | 7.0   | 3570 | 0.8514          | 0.8334    | 0.8215 | 0.8270 | 0.8645   |
+| 0.0817        | 8.0   | 4080 | 0.8539          | 0.8341    | 0.8245 | 0.8288 | 0.8660   |
+| 0.0659        | 9.0   | 4590 | 0.9233          | 0.8441    | 0.8202 | 0.8313 | 0.8655   |
+| 0.0588        | 10.0  | 5100 | 0.9426          | 0.8396    | 0.8182 | 0.8282 | 0.8655   |
 ### Framework versions