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distilhubert-finetuned-cry-detector

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  1. README.md +17 -20
  2. checkpoint-170/config.json +80 -0
  3. checkpoint-170/model.safetensors +3 -0
  4. checkpoint-170/optimizer.pt +3 -0
  5. checkpoint-170/preprocessor_config.json +9 -0
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  34. checkpoint-426/config.json +80 -0
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  50. checkpoint-595/config.json +80 -0
README.md CHANGED
@@ -26,16 +26,16 @@ model-index:
26
  metrics:
27
  - name: Accuracy
28
  type: accuracy
29
- value: 0.9852941176470589
30
  - name: F1
31
  type: f1
32
- value: 0.9853150765112866
33
  - name: Precision
34
  type: precision
35
- value: 0.9853868369053048
36
  - name: Recall
37
  type: recall
38
- value: 0.9852941176470589
39
  ---
40
 
41
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -45,11 +45,11 @@ should probably proofread and complete it, then remove this comment. -->
45
 
46
  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
47
  It achieves the following results on the evaluation set:
48
- - Loss: 0.0332
49
- - Accuracy: 0.9853
50
- - F1: 0.9853
51
- - Precision: 0.9854
52
- - Recall: 0.9853
53
 
54
  ## Model description
55
 
@@ -77,22 +77,19 @@ The following hyperparameters were used during training:
77
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
78
  - lr_scheduler_type: cosine
79
  - lr_scheduler_warmup_ratio: 0.001
80
- - num_epochs: 10
81
 
82
  ### Training results
83
 
84
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
85
  |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
86
- | No log | 0.9412 | 12 | 0.1931 | 0.9363 | 0.9365 | 0.9372 | 0.9363 |
87
- | No log | 1.9608 | 25 | 0.0950 | 0.9706 | 0.9704 | 0.9710 | 0.9706 |
88
- | No log | 2.9804 | 38 | 0.0611 | 0.9804 | 0.9804 | 0.9804 | 0.9804 |
89
- | No log | 4.0 | 51 | 0.0492 | 0.9853 | 0.9853 | 0.9853 | 0.9853 |
90
- | No log | 4.9412 | 63 | 0.0588 | 0.9804 | 0.9805 | 0.9814 | 0.9804 |
91
- | No log | 5.9608 | 76 | 0.0368 | 0.9853 | 0.9853 | 0.9854 | 0.9853 |
92
- | No log | 6.9804 | 89 | 0.0382 | 0.9902 | 0.9902 | 0.9903 | 0.9902 |
93
- | No log | 8.0 | 102 | 0.0318 | 0.9951 | 0.9951 | 0.9951 | 0.9951 |
94
- | No log | 8.9412 | 114 | 0.0331 | 0.9853 | 0.9853 | 0.9854 | 0.9853 |
95
- | No log | 9.4118 | 120 | 0.0332 | 0.9853 | 0.9853 | 0.9854 | 0.9853 |
96
 
97
 
98
  ### Framework versions
 
26
  metrics:
27
  - name: Accuracy
28
  type: accuracy
29
+ value: 0.991941391941392
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  - name: F1
31
  type: f1
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+ value: 0.9919569277165429
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  - name: Precision
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  type: precision
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+ value: 0.9920048531706146
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  - name: Recall
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  type: recall
38
+ value: 0.991941391941392
39
  ---
40
 
41
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
45
 
46
  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
47
  It achieves the following results on the evaluation set:
48
+ - Loss: 0.0408
49
+ - Accuracy: 0.9919
50
+ - F1: 0.9920
51
+ - Precision: 0.9920
52
+ - Recall: 0.9919
53
 
54
  ## Model description
55
 
 
77
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
78
  - lr_scheduler_type: cosine
79
  - lr_scheduler_warmup_ratio: 0.001
80
+ - num_epochs: 7
81
 
82
  ### Training results
83
 
84
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
85
  |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
86
+ | No log | 0.9956 | 85 | 0.0736 | 0.9788 | 0.9788 | 0.9790 | 0.9788 |
87
+ | No log | 1.9912 | 170 | 0.0680 | 0.9758 | 0.9760 | 0.9770 | 0.9758 |
88
+ | No log | 2.9985 | 256 | 0.0447 | 0.9875 | 0.9876 | 0.9876 | 0.9875 |
89
+ | No log | 3.9941 | 341 | 0.0452 | 0.9905 | 0.9905 | 0.9905 | 0.9905 |
90
+ | No log | 4.9898 | 426 | 0.0439 | 0.9919 | 0.9920 | 0.9920 | 0.9919 |
91
+ | 0.053 | 5.9971 | 512 | 0.0401 | 0.9919 | 0.9920 | 0.9920 | 0.9919 |
92
+ | 0.053 | 6.9693 | 595 | 0.0408 | 0.9919 | 0.9920 | 0.9920 | 0.9919 |
 
 
 
93
 
94
 
95
  ### Framework versions
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