File size: 2,607 Bytes
10cd890
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-music-classification
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: GTZAN
      type: marsyas/gtzan
      config: all
      split: train
      args: all
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.86
---

<!-- 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-music-classification

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: 0.7110
- Accuracy: 0.86

## 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: 8
- eval_batch_size: 8
- 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: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1284        | 1.0   | 113  | 1.9802          | 0.5      |
| 1.435         | 2.0   | 226  | 1.3403          | 0.65     |
| 1.0235        | 3.0   | 339  | 0.9941          | 0.74     |
| 0.8973        | 4.0   | 452  | 0.9184          | 0.69     |
| 0.7312        | 5.0   | 565  | 0.6918          | 0.79     |
| 0.4306        | 6.0   | 678  | 0.6343          | 0.78     |
| 0.4204        | 7.0   | 791  | 0.6174          | 0.83     |
| 0.1326        | 8.0   | 904  | 0.5888          | 0.83     |
| 0.0766        | 9.0   | 1017 | 0.5939          | 0.84     |
| 0.0308        | 10.0  | 1130 | 0.7191          | 0.86     |
| 0.0318        | 11.0  | 1243 | 0.7308          | 0.84     |
| 0.0657        | 12.0  | 1356 | 0.7222          | 0.81     |
| 0.0096        | 13.0  | 1469 | 0.7075          | 0.84     |
| 0.0077        | 14.0  | 1582 | 0.7268          | 0.84     |
| 0.0073        | 15.0  | 1695 | 0.6957          | 0.85     |
| 0.0066        | 16.0  | 1808 | 0.7110          | 0.86     |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3