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: 0.8740
-- Precision: 0.8582
-- Recall: 0.8441
-- F1: 0.8491
-- Accuracy: 0.8896
 ## 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 |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.8195        | 1.0   | 514  | 0.5442          | 0.7965    | 0.8464 | 0.8071 | 0.8638   |
-| 0.4249        | 2.0   | 1028 | 0.6446          | 0.8539    | 0.8236 | 0.8306 | 0.8769   |
-| 0.3014        | 3.0   | 1542 | 0.6167          | 0.8484    | 0.8472 | 0.8463 | 0.8818   |
-| 0.2268        | 4.0   | 2056 | 0.6262          | 0.8493    | 0.8594 | 0.8523 | 0.8896   |
-| 0.1549        | 5.0   | 2570 | 0.6261          | 0.8443    | 0.8585 | 0.8501 | 0.8862   |
-| 0.124         | 6.0   | 3084 | 0.8133          | 0.8566    | 0.8454 | 0.8503 | 0.8876   |
-| 0.1057        | 7.0   | 3598 | 0.7241          | 0.8645    | 0.8596 | 0.8584 | 0.8925   |
-| 0.0955        | 8.0   | 4112 | 0.8449          | 0.8532    | 0.8334 | 0.8421 | 0.8862   |
-| 0.0744        | 9.0   | 4626 | 0.8140          | 0.8544    | 0.8536 | 0.8527 | 0.8901   |
-| 0.0493        | 10.0  | 5140 | 0.8740          | 0.8582    | 0.8441 | 0.8491 | 0.8896   |
 ### 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: 0.8909
+- Precision: 0.8485
+- Recall: 0.8467
+- F1: 0.8467
+- Accuracy: 0.8857
 ## 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 |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.9219        | 1.0   | 514  | 0.5751          | 0.8031    | 0.8211 | 0.8091 | 0.8658   |
+| 0.4593        | 2.0   | 1028 | 0.5435          | 0.8461    | 0.8372 | 0.8333 | 0.8789   |
+| 0.2853        | 3.0   | 1542 | 0.6109          | 0.8362    | 0.8385 | 0.8347 | 0.8794   |
+| 0.2159        | 4.0   | 2056 | 0.6320          | 0.8349    | 0.8681 | 0.8492 | 0.8847   |
+| 0.1531        | 5.0   | 2570 | 0.6988          | 0.8564    | 0.8532 | 0.8536 | 0.8901   |
+| 0.1223        | 6.0   | 3084 | 0.8081          | 0.8447    | 0.8476 | 0.8456 | 0.8833   |
+| 0.1073        | 7.0   | 3598 | 0.7644          | 0.8366    | 0.8501 | 0.8425 | 0.8833   |
+| 0.0958        | 8.0   | 4112 | 0.8606          | 0.8522    | 0.8468 | 0.8488 | 0.8847   |
+| 0.0769        | 9.0   | 4626 | 0.8468          | 0.8475    | 0.8482 | 0.8472 | 0.8857   |
+| 0.061         | 10.0  | 5140 | 0.8909          | 0.8485    | 0.8467 | 0.8467 | 0.8857   |
 ### Framework versions