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# 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: 0.6694
- 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.99  | 56   | 1.9426          | 0.5      |
| No log        | 1.99  | 112  | 1.4157          | 0.63     |
| No log        | 2.99  | 168  | 1.1351          | 0.69     |
| No log        | 3.99  | 224  | 1.0285          | 0.72     |
| No log        | 4.99  | 280  | 0.8538          | 0.79     |
| No log        | 5.99  | 336  | 0.8015          | 0.74     |
| No log        | 6.99  | 392  | 0.6694          | 0.82     |
| No log        | 7.99  | 448  | 0.6779          | 0.79     |
| 1.0811        | 8.99  | 504  | 0.6414          | 0.81     |
| 1.0811        | 9.99  | 560  | 0.6443          | 0.82     |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.11.0
- Datasets 2.6.1
- Tokenizers 0.11.6

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

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


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: 0.6694
* 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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.24.0
* Pytorch 1.11.0
* Datasets 2.6.1
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.11.0
* Datasets 2.6.1
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #hubert #audio-classification #hf-course #generated_from_trainer #dataset-marsyas/gtzan #license-apache-2.0 #endpoints_compatible #has_space #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
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.11.0
* Datasets 2.6.1
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #hubert #audio-classification #hf-course #generated_from_trainer #dataset-marsyas/gtzan #license-apache-2.0 #endpoints_compatible #has_space #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
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.24.0
* Pytorch 1.11.0
* Datasets 2.6.1
* Tokenizers 0.11.6