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# hubert-base-ls960

This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the galsenai/waxal_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1857
- Accuracy: 0.6442
- Precision: 0.8369
- F1: 0.7121

## 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: 30
- eval_batch_size: 30
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 120
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 32.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 4.523         | 2.53  | 500  | 5.1547          | 0.0205   | 0.0047    | 0.0037 |
| 3.4187        | 5.05  | 1000 | 4.6287          | 0.0337   | 0.0256    | 0.0163 |
| 2.3533        | 7.58  | 1500 | 4.2550          | 0.0944   | 0.1033    | 0.0641 |
| 1.7145        | 10.1  | 2000 | 3.9540          | 0.1095   | 0.2091    | 0.0964 |
| 1.3245        | 12.63 | 2500 | 3.8557          | 0.1758   | 0.3609    | 0.1859 |
| 1.0729        | 15.15 | 3000 | 3.7411          | 0.2247   | 0.4918    | 0.2537 |
| 0.8955        | 17.68 | 3500 | 3.2683          | 0.3789   | 0.6162    | 0.4256 |
| 0.7697        | 20.2  | 4000 | 2.8749          | 0.4612   | 0.7106    | 0.5171 |
| 0.6864        | 22.73 | 4500 | 2.7251          | 0.5169   | 0.7437    | 0.5779 |
| 0.6061        | 25.25 | 5000 | 2.5061          | 0.5631   | 0.8043    | 0.6335 |
| 0.5777        | 27.78 | 5500 | 2.2830          | 0.6177   | 0.8183    | 0.6837 |
| 0.5304        | 30.3  | 6000 | 2.1857          | 0.6442   | 0.8369    | 0.7121 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer", "wolof"], "metrics": ["accuracy", "precision", "f1"], "model-index": [{"name": "hubert-base-ls960", "results": []}]}
transformers pytorch hubert audio-classification generated_from_trainer wolof license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #hubert #audio-classification #generated_from_trainer #wolof #license-apache-2.0 #endpoints_compatible #region-us 

hubert-base-ls960
=================


This model is a fine-tuned version of facebook/hubert-base-ls960 on the galsenai/waxal\_dataset dataset.
It achieves the following results on the evaluation set:


* Loss: 2.1857
* Accuracy: 0.6442
* Precision: 0.8369
* F1: 0.7121


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: 30
* eval\_batch\_size: 30
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 120
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 32.0


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 30
* eval\_batch\_size: 30
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 120
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 32.0 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #hubert #audio-classification #generated_from_trainer #wolof #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 30
* eval\_batch\_size: 30
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 120
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 32.0 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #hubert #audio-classification #generated_from_trainer #wolof #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 30
* eval\_batch\_size: 30
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 120
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 32.0### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2