File size: 3,564 Bytes
869bff3 02c0ec1 869bff3 02c0ec1 869bff3 0291642 869bff3 02c0ec1 869bff3 02c0ec1 869bff3 |
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 |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: music-genre-classifer-20-finetuned-gtzan
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: 0.82
---
<!-- 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. -->
# music-genre-classifer-20-finetuned-gtzan
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: 0.5510
- 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: 2e-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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.201 | 1.0 | 113 | 0.39 | 2.1256 |
| 1.6789 | 2.0 | 226 | 0.59 | 1.6543 |
| 1.5602 | 3.0 | 339 | 0.64 | 1.3917 |
| 1.1966 | 4.0 | 452 | 0.67 | 1.1946 |
| 1.1131 | 5.0 | 565 | 0.77 | 1.0492 |
| 1.0258 | 6.0 | 678 | 0.76 | 0.9712 |
| 0.988 | 7.0 | 791 | 0.76 | 0.9160 |
| 0.7303 | 8.0 | 904 | 0.8 | 0.8704 |
| 0.8036 | 9.0 | 1017 | 0.8 | 0.8425 |
| 0.742 | 10.0 | 1130 | 0.81 | 0.8224 |
| 0.7463 | 11.0 | 1243 | 0.81 | 0.8140 |
| 0.7428 | 12.0 | 1356 | 0.78 | 0.8112 |
| 0.6081 | 13.0 | 1469 | 0.82 | 0.6975 |
| 0.8154 | 14.0 | 1582 | 0.84 | 0.6636 |
| 0.3758 | 15.0 | 1695 | 0.84 | 0.6215 |
| 0.503 | 16.0 | 1808 | 0.81 | 0.6251 |
| 0.4542 | 17.0 | 1921 | 0.84 | 0.5869 |
| 0.3285 | 18.0 | 2034 | 0.85 | 0.5830 |
| 0.4309 | 19.0 | 2147 | 0.82 | 0.5844 |
| 0.342 | 20.0 | 2260 | 0.85 | 0.5840 |
| 0.3051 | 21.0 | 2373 | 0.83 | 0.5843 |
| 0.3558 | 22.0 | 2486 | 0.6144 | 0.79 |
| 0.3371 | 23.0 | 2599 | 0.5673 | 0.81 |
| 0.2882 | 24.0 | 2712 | 0.5365 | 0.84 |
| 0.2326 | 25.0 | 2825 | 0.5848 | 0.83 |
| 0.192 | 26.0 | 2938 | 0.5406 | 0.85 |
| 0.1528 | 27.0 | 3051 | 0.5482 | 0.82 |
| 0.1937 | 28.0 | 3164 | 0.5448 | 0.84 |
| 0.1264 | 29.0 | 3277 | 0.5487 | 0.84 |
| 0.1356 | 30.0 | 3390 | 0.5510 | 0.82 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|