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: 0.9426
-- Precision: 0.8396
-- Recall: 0.8182
-- F1: 0.8282
-- Accuracy: 0.8655
 ## 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: 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 |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.9585        | 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

 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: 0.9072
+- Precision: 0.8332
+- Recall: 0.8192
+- F1: 0.8259
+- Accuracy: 0.8660
 ## Model description
 ### Training hyperparameters
 The following hyperparameters were used during training:
+- learning_rate: 2e-05
 - train_batch_size: 16
 - eval_batch_size: 16
 - seed: 42
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.0741        | 1.0   | 510  | 0.6328          | 0.7761    | 0.8093 | 0.7820 | 0.8292   |
+| 0.465         | 2.0   | 1020 | 0.5751          | 0.8326    | 0.8237 | 0.8265 | 0.8625   |
+| 0.2979        | 3.0   | 1530 | 0.5442          | 0.8285    | 0.8482 | 0.8370 | 0.8719   |
+| 0.2312        | 4.0   | 2040 | 0.6811          | 0.8434    | 0.8298 | 0.8350 | 0.8665   |
+| 0.1609        | 5.0   | 2550 | 0.6873          | 0.8216    | 0.8338 | 0.8271 | 0.8635   |
+| 0.14          | 6.0   | 3060 | 0.8476          | 0.8386    | 0.8175 | 0.8265 | 0.8640   |
+| 0.1135        | 7.0   | 3570 | 0.8456          | 0.8302    | 0.8202 | 0.8249 | 0.8630   |
+| 0.0973        | 8.0   | 4080 | 0.8595          | 0.8307    | 0.8186 | 0.8243 | 0.8625   |
+| 0.0758        | 9.0   | 4590 | 0.8828          | 0.8306    | 0.8201 | 0.8251 | 0.8655   |
+| 0.0669        | 10.0  | 5100 | 0.9072          | 0.8332    | 0.8192 | 0.8259 | 0.8660   |
 ### Framework versions