End of training

README.md

@@ -6,36 +6,9 @@ tags:
 - generated_from_trainer
 datasets:
 - audiofolder
-metrics:
-- accuracy
-- f1
-- precision
-- recall
 model-index:
 - name: distilhubert-finetuned-mixed-data
-  results:
-  - task:
-      name: Audio Classification
-      type: audio-classification
-    dataset:
-      name: audiofolder
-      type: audiofolder
-      config: default
-      split: None
-      args: default
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.889261744966443
-    - name: F1
-      type: f1
-      value: 0.8885260378939167
-    - name: Precision
-      type: precision
-      value: 0.887959005693052
-    - name: Recall
-      type: recall
-      value: 0.889261744966443
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,12 +17,6 @@ should probably proofread and complete it, then remove this comment. -->
 # distilhubert-finetuned-mixed-data
 
 This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.3033
-- Accuracy: 0.8893
-- F1: 0.8885
-- Precision: 0.8880
-- Recall: 0.8893
 
 ## Model description
 
@@ -77,22 +44,13 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: cosine
 - lr_scheduler_warmup_ratio: 0.001
-- num_epochs: 10
+- num_epochs: 1
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
-|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| No log        | 0.9664 | 18   | 0.6703          | 0.7819   | 0.7264 | 0.6898    | 0.7819 |
-| No log        | 1.9866 | 37   | 0.5053          | 0.7752   | 0.7203 | 0.6849    | 0.7752 |
-| No log        | 2.9530 | 55   | 0.4473          | 0.7987   | 0.7457 | 0.7193    | 0.7987 |
-| No log        | 3.9732 | 74   | 0.3820          | 0.8691   | 0.8578 | 0.8854    | 0.8691 |
-| No log        | 4.9933 | 93   | 0.3435          | 0.8691   | 0.8671 | 0.8704    | 0.8691 |
-| No log        | 5.9597 | 111  | 0.3176          | 0.8859   | 0.8855 | 0.8868    | 0.8859 |
-| No log        | 6.9799 | 130  | 0.3045          | 0.8859   | 0.8853 | 0.8850    | 0.8859 |
-| No log        | 8.0    | 149  | 0.3051          | 0.8859   | 0.8849 | 0.8843    | 0.8859 |
-| No log        | 8.9664 | 167  | 0.3031          | 0.8926   | 0.8913 | 0.8910    | 0.8926 |
-| No log        | 9.6644 | 180  | 0.3033          | 0.8893   | 0.8885 | 0.8880    | 0.8893 |
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| No log        | 0.9664 | 18   | 0.9747          | 0.7282   |
 
 
 ### Framework versions

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