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# distilhubert-finetuned-gtzan-v3

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: 0.5752
- Accuracy: 0.83

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9108        | 1.0   | 113  | 1.9472          | 0.43     |
| 1.3286        | 2.0   | 226  | 1.4173          | 0.65     |
| 1.032         | 3.0   | 339  | 0.9815          | 0.67     |
| 0.726         | 4.0   | 452  | 0.7403          | 0.79     |
| 0.4621        | 5.0   | 565  | 0.6390          | 0.8      |
| 0.3439        | 6.0   | 678  | 0.5248          | 0.85     |
| 0.1592        | 7.0   | 791  | 0.4861          | 0.86     |
| 0.1283        | 8.0   | 904  | 0.4995          | 0.87     |
| 0.1191        | 9.0   | 1017 | 0.4804          | 0.87     |
| 0.0236        | 10.0  | 1130 | 0.6737          | 0.8      |
| 0.0146        | 11.0  | 1243 | 0.6211          | 0.81     |
| 0.0105        | 12.0  | 1356 | 0.5806          | 0.86     |
| 0.008         | 13.0  | 1469 | 0.5645          | 0.84     |
| 0.0082        | 14.0  | 1582 | 0.6033          | 0.83     |
| 0.0072        | 15.0  | 1695 | 0.5752          | 0.83     |


### Framework versions

- Transformers 4.30.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["marsyas/gtzan"], "metrics": ["accuracy"], "model-index": [{"name": "distilhubert-finetuned-gtzan-v3", "results": []}]}
transformers pytorch tensorboard hubert audio-classification generated_from_trainer dataset:marsyas/gtzan license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #license-apache-2.0 #endpoints_compatible #region-us 

distilhubert-finetuned-gtzan-v3
===============================


This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5752
* Accuracy: 0.83


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: 15


### Training results



### Framework versions


* Transformers 4.30.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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: 15 ### Training results ### Framework versions


* Transformers 4.30.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #license-apache-2.0 #endpoints_compatible #region-us 
 ### 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: 15 ### Training results ### Framework versions


* Transformers 4.30.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3