File size: 2,938 Bytes
338ac52 aec2877 b17058e 338ac52 b17058e 338ac52 b17058e 96d6a4e 338ac52 0dd5758 338ac52 b17058e 338ac52 96d6a4e 338ac52 b17058e 96d6a4e b17058e afbcb6d b17058e d4d28da 338ac52 b17058e 96d6a4e b17058e 338ac52 d16bdf4 b17058e |
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 |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- audio-classification
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-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: 0.86
---
<!-- 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-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: 0.5120
- 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2584 | 1.0 | 57 | 2.2062 | 0.35 |
| 1.8611 | 2.0 | 114 | 1.7924 | 0.53 |
| 1.4492 | 3.0 | 171 | 1.3901 | 0.65 |
| 1.0971 | 4.0 | 228 | 1.1676 | 0.69 |
| 0.9848 | 5.0 | 285 | 0.9750 | 0.74 |
| 0.8434 | 6.0 | 342 | 0.8434 | 0.74 |
| 0.7321 | 7.0 | 399 | 0.7555 | 0.83 |
| 0.5364 | 8.0 | 456 | 0.6995 | 0.79 |
| 0.4557 | 9.0 | 513 | 0.6118 | 0.84 |
| 0.4166 | 10.0 | 570 | 0.5975 | 0.83 |
| 0.2729 | 11.0 | 627 | 0.5576 | 0.83 |
| 0.2491 | 12.0 | 684 | 0.5737 | 0.82 |
| 0.2211 | 13.0 | 741 | 0.5129 | 0.84 |
| 0.1243 | 14.0 | 798 | 0.5710 | 0.83 |
| 0.0904 | 15.0 | 855 | 0.5087 | 0.86 |
| 0.0773 | 16.0 | 912 | 0.5836 | 0.8 |
| 0.0598 | 17.0 | 969 | 0.4871 | 0.83 |
| 0.0551 | 18.0 | 1026 | 0.4865 | 0.84 |
| 0.0467 | 19.0 | 1083 | 0.5043 | 0.84 |
| 0.0364 | 20.0 | 1140 | 0.5120 | 0.86 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1
|