End of training

README.md

@@ -7,6 +7,8 @@ tags:
 datasets:
 - audiofolder
 metrics:
+- precision
+- recall
 - f1
 model-index:
 - name: distilhubert-finetuned-speech_commands-finetuned-gtzan
@@ -18,12 +20,18 @@ model-index:
       name: audiofolder
       type: audiofolder
       config: default
-      split: validation
+      split: test
       args: default
     metrics:
+    - name: Precision
+      type: precision
+      value: 0.9759184555734861
+    - name: Recall
+      type: recall
+      value: 0.9749126053876208
     - name: F1
       type: f1
-      value: 0.9799704307080909
+      value: 0.9749296122020006
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -33,8 +41,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [imrajeshkr/distilhubert-finetuned-speech_commands](https://huggingface.co/imrajeshkr/distilhubert-finetuned-speech_commands) on the audiofolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0751
-- F1: 0.9800
+- Loss: 0.0934
+- Precision: 0.9759
+- Recall: 0.9749
+- F1: 0.9749
 
 ## Model description
 
@@ -65,13 +75,13 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | F1     |
-|:-------------:|:-----:|:----:|:---------------:|:------:|
-| 0.2364        | 1.0   | 1216 | 0.2190          | 0.9311 |
-| 0.1287        | 2.0   | 2432 | 0.1014          | 0.9678 |
-| 0.0445        | 3.0   | 3648 | 0.0743          | 0.9778 |
-| 0.0037        | 4.0   | 4864 | 0.0785          | 0.9773 |
-| 0.0087        | 5.0   | 6080 | 0.0751          | 0.9800 |
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
+| 0.2713        | 1.0   | 1216 | 0.2523          | 0.9172    | 0.9267 | 0.9166 |
+| 0.137         | 2.0   | 2432 | 0.1119          | 0.9685    | 0.9667 | 0.9664 |
+| 0.0295        | 3.0   | 3648 | 0.0977          | 0.9726    | 0.9703 | 0.9701 |
+| 0.0037        | 4.0   | 4864 | 0.0956          | 0.9743    | 0.9733 | 0.9732 |
+| 0.052         | 5.0   | 6080 | 0.0934          | 0.9759    | 0.9749 | 0.9749 |
 
 
 ### Framework versions