End of training

README.md

@@ -25,13 +25,13 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9661601051155746
+      value: 0.9743628199079283
     - name: Recall
       type: recall
-      value: 0.9662664379645511
+      value: 0.9743424814179531
     - name: F1
       type: f1
-      value: 0.9661541075893276
+      value: 0.9743165983480835
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,10 +41,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [MIT/ast-finetuned-audioset-12-12-0.447](https://huggingface.co/MIT/ast-finetuned-audioset-12-12-0.447) on the audiofolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1790
-- Precision: 0.9662
-- Recall: 0.9663
-- F1: 0.9662
+- Loss: 0.1346
+- Precision: 0.9744
+- Recall: 0.9743
+- F1: 0.9743
 
 ## Model description
 
@@ -75,13 +75,13 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step  | F1     | Validation Loss | Precision | Recall |
-|:-------------:|:-----:|:-----:|:------:|:---------------:|:---------:|:------:|
-| 0.0795        | 1.0   | 3496  | 0.9342 | 0.2169          | 0.9357    | 0.9347 |
-| 0.1295        | 2.0   | 6992  | 0.9467 | 0.1728          | 0.9486    | 0.9473 |
-| 0.0279        | 3.0   | 10488 | 0.9551 | 0.1717          | 0.9558    | 0.9556 |
-| 0.0029        | 4.0   | 13984 | 0.9621 | 0.1733          | 0.9624    | 0.9621 |
-| 0.0023        | 5.0   | 17480 | 0.9662 | 0.1790          | 0.9663    | 0.9662 |
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|
+| 0.0799        | 1.0   | 3496  | 0.1498          | 0.9596    | 0.9573 | 0.9577 |
+| 0.0624        | 2.0   | 6992  | 0.1141          | 0.9689    | 0.9687 | 0.9685 |
+| 0.0091        | 3.0   | 10488 | 0.1285          | 0.9713    | 0.9713 | 0.9711 |
+| 0.0384        | 4.0   | 13984 | 0.1237          | 0.9743    | 0.9743 | 0.9742 |
+| 0.0019        | 5.0   | 17480 | 0.1346          | 0.9744    | 0.9743 | 0.9743 |
 
 
 ### Framework versions