File size: 6,700 Bytes
67d2bc1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-4
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: GTZAN
      type: marsyas/gtzan
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.82
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilhubert-finetuned-gtzan-4

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8452
- Accuracy: 0.82

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 80

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.2676        | 1.0   | 225   | 2.2400          | 0.33     |
| 1.9086        | 2.0   | 450   | 1.8716          | 0.55     |
| 1.417         | 3.0   | 675   | 1.4094          | 0.69     |
| 1.0484        | 4.0   | 900   | 1.1065          | 0.77     |
| 1.0122        | 5.0   | 1125  | 0.9172          | 0.75     |
| 0.8937        | 6.0   | 1350  | 1.1343          | 0.66     |
| 0.2979        | 7.0   | 1575  | 0.6102          | 0.83     |
| 0.3413        | 8.0   | 1800  | 1.1212          | 0.71     |
| 1.459         | 9.0   | 2025  | 1.5433          | 0.75     |
| 0.5177        | 10.0  | 2250  | 1.3990          | 0.78     |
| 0.0494        | 11.0  | 2475  | 2.7712          | 0.78     |
| 0.792         | 12.0  | 2700  | 2.7047          | 0.81     |
| 0.0           | 13.0  | 2925  | 2.8097          | 0.82     |
| 0.0           | 14.0  | 3150  | 3.3873          | 0.79     |
| 0.0           | 15.0  | 3375  | 2.6185          | 0.81     |
| 0.0           | 16.0  | 3600  | 3.0773          | 0.81     |
| 0.0           | 17.0  | 3825  | 2.2380          | 0.84     |
| 0.0           | 18.0  | 4050  | 2.5949          | 0.79     |
| 0.0           | 19.0  | 4275  | 3.4890          | 0.75     |
| 0.0           | 20.0  | 4500  | 2.7776          | 0.82     |
| 0.0           | 21.0  | 4725  | 3.8952          | 0.77     |
| 0.0           | 22.0  | 4950  | 2.8020          | 0.8      |
| 0.0           | 23.0  | 5175  | 3.7968          | 0.72     |
| 0.0           | 24.0  | 5400  | 2.8630          | 0.81     |
| 0.0           | 25.0  | 5625  | 2.2026          | 0.84     |
| 0.0           | 26.0  | 5850  | 1.8612          | 0.87     |
| 0.0           | 27.0  | 6075  | 3.2650          | 0.78     |
| 0.0           | 28.0  | 6300  | 2.6739          | 0.82     |
| 0.0           | 29.0  | 6525  | 3.0167          | 0.8      |
| 0.0           | 30.0  | 6750  | 1.9316          | 0.84     |
| 0.0           | 31.0  | 6975  | 3.0458          | 0.8      |
| 0.0           | 32.0  | 7200  | 3.3569          | 0.77     |
| 0.0           | 33.0  | 7425  | 3.0012          | 0.79     |
| 0.0           | 34.0  | 7650  | 3.2477          | 0.79     |
| 0.0           | 35.0  | 7875  | 3.2145          | 0.79     |
| 0.0           | 36.0  | 8100  | 3.0645          | 0.79     |
| 0.0           | 37.0  | 8325  | 3.2974          | 0.77     |
| 0.0           | 38.0  | 8550  | 3.4422          | 0.77     |
| 0.0           | 39.0  | 8775  | 2.7268          | 0.8      |
| 0.0           | 40.0  | 9000  | 2.6908          | 0.8      |
| 0.0           | 41.0  | 9225  | 2.4034          | 0.82     |
| 0.0           | 42.0  | 9450  | 3.1446          | 0.79     |
| 0.0           | 43.0  | 9675  | 2.9127          | 0.8      |
| 0.0           | 44.0  | 9900  | 2.3812          | 0.81     |
| 0.0           | 45.0  | 10125 | 2.4215          | 0.81     |
| 0.0           | 46.0  | 10350 | 2.6125          | 0.82     |
| 0.7338        | 47.0  | 10575 | 2.5113          | 0.82     |
| 0.0           | 48.0  | 10800 | 2.9264          | 0.81     |
| 0.0           | 49.0  | 11025 | 2.7811          | 0.81     |
| 0.0           | 50.0  | 11250 | 2.6749          | 0.8      |
| 0.0           | 51.0  | 11475 | 3.2003          | 0.78     |
| 0.0           | 52.0  | 11700 | 3.2670          | 0.78     |
| 0.0           | 53.0  | 11925 | 3.4001          | 0.76     |
| 0.0           | 54.0  | 12150 | 2.8570          | 0.76     |
| 0.0           | 55.0  | 12375 | 2.1772          | 0.83     |
| 0.0           | 56.0  | 12600 | 2.7977          | 0.81     |
| 0.0           | 57.0  | 12825 | 2.9106          | 0.78     |
| 0.0           | 58.0  | 13050 | 2.8428          | 0.8      |
| 0.0           | 59.0  | 13275 | 2.7308          | 0.78     |
| 0.0           | 60.0  | 13500 | 2.8214          | 0.8      |
| 0.0           | 61.0  | 13725 | 2.8194          | 0.79     |
| 0.0           | 62.0  | 13950 | 1.7708          | 0.85     |
| 0.0           | 63.0  | 14175 | 2.6017          | 0.81     |
| 0.0           | 64.0  | 14400 | 2.7698          | 0.78     |
| 0.0           | 65.0  | 14625 | 2.8218          | 0.81     |
| 0.0           | 66.0  | 14850 | 2.8252          | 0.82     |
| 0.0           | 67.0  | 15075 | 2.9149          | 0.81     |
| 0.0           | 68.0  | 15300 | 2.8106          | 0.81     |
| 0.0           | 69.0  | 15525 | 2.8514          | 0.8      |
| 0.0           | 70.0  | 15750 | 2.6649          | 0.82     |
| 0.0           | 71.0  | 15975 | 2.5629          | 0.81     |
| 0.0           | 72.0  | 16200 | 2.8140          | 0.81     |
| 0.0           | 73.0  | 16425 | 2.8164          | 0.79     |
| 0.0           | 74.0  | 16650 | 2.7022          | 0.81     |
| 0.0           | 75.0  | 16875 | 2.7376          | 0.81     |
| 0.0           | 76.0  | 17100 | 2.6498          | 0.8      |
| 0.0           | 77.0  | 17325 | 2.7363          | 0.81     |
| 0.0           | 78.0  | 17550 | 2.8057          | 0.81     |
| 0.0           | 79.0  | 17775 | 2.8526          | 0.81     |
| 0.0           | 80.0  | 18000 | 2.8452          | 0.82     |


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3