knapierk
/

dream_classifier

@@ -16,8 +16,8 @@ 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: 1.3533
-- Accuracy: 0.7321
 ## Model description
@@ -48,26 +48,26 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 17   | 0.5774          | 0.7321   |
-| No log        | 2.0   | 34   | 0.5660          | 0.7321   |
-| No log        | 3.0   | 51   | 0.5903          | 0.7321   |
-| No log        | 4.0   | 68   | 0.5558          | 0.7321   |
-| No log        | 5.0   | 85   | 0.5904          | 0.7411   |
-| No log        | 6.0   | 102  | 0.6088          | 0.7321   |
-| No log        | 7.0   | 119  | 0.7359          | 0.7589   |
-| No log        | 8.0   | 136  | 0.9027          | 0.7589   |
-| No log        | 9.0   | 153  | 1.0194          | 0.7411   |
-| No log        | 10.0  | 170  | 1.1241          | 0.7589   |
-| No log        | 11.0  | 187  | 1.1849          | 0.7411   |
-| No log        | 12.0  | 204  | 1.2305          | 0.7411   |
-| No log        | 13.0  | 221  | 1.2551          | 0.75     |
-| No log        | 14.0  | 238  | 1.2859          | 0.7411   |
-| No log        | 15.0  | 255  | 1.3070          | 0.7411   |
-| No log        | 16.0  | 272  | 1.3238          | 0.7411   |
-| No log        | 17.0  | 289  | 1.3357          | 0.7411   |
-| No log        | 18.0  | 306  | 1.3449          | 0.7411   |
-| No log        | 19.0  | 323  | 1.3513          | 0.7321   |
-| No log        | 20.0  | 340  | 1.3533          | 0.7321   |
 ### 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: 0.6082
+- Accuracy: 0.8661
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 17   | 0.6040          | 0.8125   |
+| No log        | 2.0   | 34   | 0.4455          | 0.8482   |
+| No log        | 3.0   | 51   | 0.3701          | 0.875    |
+| No log        | 4.0   | 68   | 0.4277          | 0.8304   |
+| No log        | 5.0   | 85   | 0.4477          | 0.8393   |
+| No log        | 6.0   | 102  | 0.3747          | 0.8929   |
+| No log        | 7.0   | 119  | 0.4383          | 0.9018   |
+| No log        | 8.0   | 136  | 0.5102          | 0.875    |
+| No log        | 9.0   | 153  | 0.5384          | 0.8661   |
+| No log        | 10.0  | 170  | 0.6888          | 0.8571   |
+| No log        | 11.0  | 187  | 0.5788          | 0.8661   |
+| No log        | 12.0  | 204  | 0.5933          | 0.8571   |
+| No log        | 13.0  | 221  | 0.6214          | 0.8661   |
+| No log        | 14.0  | 238  | 0.6246          | 0.8661   |
+| No log        | 15.0  | 255  | 0.6033          | 0.8661   |
+| No log        | 16.0  | 272  | 0.6066          | 0.8661   |
+| No log        | 17.0  | 289  | 0.6092          | 0.8661   |
+| No log        | 18.0  | 306  | 0.6079          | 0.8661   |
+| No log        | 19.0  | 323  | 0.6080          | 0.8661   |
+| No log        | 20.0  | 340  | 0.6082          | 0.8661   |
 ### Framework versions