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---
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: 0.775
---
<!-- 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: 1.2754
- Accuracy: 0.775
## 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: 12
- eval_batch_size: 12
- 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2856 | 1.0 | 67 | 2.2801 | 0.19 |
| 2.1936 | 2.0 | 134 | 2.1829 | 0.335 |
| 1.9496 | 3.0 | 201 | 1.9189 | 0.5 |
| 1.6727 | 4.0 | 268 | 1.6280 | 0.595 |
| 1.5444 | 5.0 | 335 | 1.4530 | 0.635 |
| 1.0974 | 6.0 | 402 | 1.2269 | 0.67 |
| 1.0647 | 7.0 | 469 | 1.0802 | 0.72 |
| 0.8521 | 8.0 | 536 | 0.9819 | 0.72 |
| 0.7618 | 9.0 | 603 | 0.9660 | 0.74 |
| 0.5022 | 10.0 | 670 | 0.8664 | 0.75 |
| 0.4576 | 11.0 | 737 | 0.8972 | 0.7 |
| 0.2801 | 12.0 | 804 | 0.8073 | 0.76 |
| 0.2404 | 13.0 | 871 | 0.7892 | 0.765 |
| 0.1493 | 14.0 | 938 | 0.8512 | 0.74 |
| 0.0945 | 15.0 | 1005 | 0.8876 | 0.74 |
| 0.049 | 16.0 | 1072 | 0.9735 | 0.72 |
| 0.0311 | 17.0 | 1139 | 0.9881 | 0.76 |
| 0.0225 | 18.0 | 1206 | 1.0965 | 0.735 |
| 0.0164 | 19.0 | 1273 | 1.0578 | 0.76 |
| 0.0124 | 20.0 | 1340 | 1.0298 | 0.75 |
| 0.0109 | 21.0 | 1407 | 1.0762 | 0.745 |
| 0.0085 | 22.0 | 1474 | 1.1168 | 0.75 |
| 0.0071 | 23.0 | 1541 | 1.1697 | 0.73 |
| 0.0063 | 24.0 | 1608 | 1.1204 | 0.765 |
| 0.0054 | 25.0 | 1675 | 1.1270 | 0.765 |
| 0.005 | 26.0 | 1742 | 1.1315 | 0.76 |
| 0.0521 | 27.0 | 1809 | 1.1868 | 0.755 |
| 0.004 | 28.0 | 1876 | 1.1645 | 0.77 |
| 0.0468 | 29.0 | 1943 | 1.1515 | 0.775 |
| 0.0036 | 30.0 | 2010 | 1.1655 | 0.775 |
| 0.0595 | 31.0 | 2077 | 1.2069 | 0.76 |
| 0.003 | 32.0 | 2144 | 1.2012 | 0.77 |
| 0.0029 | 33.0 | 2211 | 1.2369 | 0.755 |
| 0.0027 | 34.0 | 2278 | 1.2397 | 0.765 |
| 0.0026 | 35.0 | 2345 | 1.2581 | 0.765 |
| 0.029 | 36.0 | 2412 | 1.2226 | 0.76 |
| 0.0024 | 37.0 | 2479 | 1.1833 | 0.775 |
| 0.0023 | 38.0 | 2546 | 1.2723 | 0.765 |
| 0.0023 | 39.0 | 2613 | 1.2575 | 0.77 |
| 0.0284 | 40.0 | 2680 | 1.2945 | 0.76 |
| 0.002 | 41.0 | 2747 | 1.2345 | 0.765 |
| 0.0203 | 42.0 | 2814 | 1.2607 | 0.77 |
| 0.002 | 43.0 | 2881 | 1.2945 | 0.765 |
| 0.0019 | 44.0 | 2948 | 1.2487 | 0.77 |
| 0.0018 | 45.0 | 3015 | 1.2626 | 0.78 |
| 0.0018 | 46.0 | 3082 | 1.2692 | 0.77 |
| 0.0017 | 47.0 | 3149 | 1.2783 | 0.77 |
| 0.0018 | 48.0 | 3216 | 1.2813 | 0.775 |
| 0.0017 | 49.0 | 3283 | 1.2861 | 0.775 |
| 0.0275 | 50.0 | 3350 | 1.2754 | 0.775 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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