File size: 2,914 Bytes
e9a1552 c8dc207 e9a1552 f13b86d e9a1552 f13b86d 7a39fe1 e9a1552 c8dc207 f13b86d 7a39fe1 e9a1552 7a39fe1 c8dc207 e9a1552 7a39fe1 e9a1552 f13b86d 7a39fe1 f13b86d e9a1552 c8dc207 e9a1552 |
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 |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- 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: 0.82
---
<!-- 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
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: 0.6623
- 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: 3e-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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2457 | 1.0 | 113 | 2.1827 | 0.33 |
| 1.8385 | 2.0 | 226 | 1.6935 | 0.61 |
| 1.46 | 3.0 | 339 | 1.4282 | 0.63 |
| 1.1508 | 4.0 | 452 | 1.1055 | 0.7 |
| 0.9972 | 5.0 | 565 | 0.8945 | 0.74 |
| 0.7826 | 6.0 | 678 | 0.7784 | 0.77 |
| 0.6802 | 7.0 | 791 | 0.7184 | 0.8 |
| 0.4635 | 8.0 | 904 | 0.7725 | 0.76 |
| 0.3746 | 9.0 | 1017 | 0.5875 | 0.84 |
| 0.264 | 10.0 | 1130 | 0.7612 | 0.75 |
| 0.1995 | 11.0 | 1243 | 0.6099 | 0.81 |
| 0.135 | 12.0 | 1356 | 0.6306 | 0.81 |
| 0.0974 | 13.0 | 1469 | 0.5947 | 0.83 |
| 0.0563 | 14.0 | 1582 | 0.7485 | 0.8 |
| 0.0443 | 15.0 | 1695 | 0.6977 | 0.79 |
| 0.0565 | 16.0 | 1808 | 0.6331 | 0.83 |
| 0.0295 | 17.0 | 1921 | 0.6538 | 0.82 |
| 0.0178 | 18.0 | 2034 | 0.6977 | 0.82 |
| 0.0191 | 19.0 | 2147 | 0.6453 | 0.83 |
| 0.0147 | 20.0 | 2260 | 0.6623 | 0.82 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|