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# wavlm-basic_n-f-n_8batch_5sec_0.0001lr_unfrozen

This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0704
- Accuracy: 0.7333
- F1: 0.7308

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.003
- num_epochs: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.3031        | 0.98  | 24   | 2.3002          | 0.1667   | 0.1148 |
| 2.2766        | 2.0   | 49   | 2.2805          | 0.15     | 0.0930 |
| 2.2298        | 2.98  | 73   | 2.0679          | 0.2333   | 0.1421 |
| 1.9839        | 4.0   | 98   | 1.8757          | 0.25     | 0.1380 |
| 1.7495        | 4.98  | 122  | 1.5981          | 0.4      | 0.3370 |
| 1.5318        | 6.0   | 147  | 1.4640          | 0.45     | 0.3698 |
| 1.2765        | 6.98  | 171  | 1.3181          | 0.5167   | 0.4437 |
| 1.261         | 8.0   | 196  | 1.0905          | 0.5833   | 0.5429 |
| 1.078         | 8.98  | 220  | 1.0944          | 0.55     | 0.5244 |
| 0.9116        | 10.0  | 245  | 0.8228          | 0.6167   | 0.5603 |
| 0.8973        | 10.98 | 269  | 0.8632          | 0.5833   | 0.5266 |
| 0.8033        | 12.0  | 294  | 0.9061          | 0.65     | 0.6398 |
| 0.7183        | 12.98 | 318  | 0.8047          | 0.7      | 0.6877 |
| 0.7526        | 14.0  | 343  | 0.6695          | 0.7333   | 0.7176 |
| 0.6381        | 14.98 | 367  | 0.7510          | 0.7833   | 0.7788 |
| 0.5266        | 16.0  | 392  | 0.6154          | 0.8      | 0.7901 |
| 0.4485        | 16.98 | 416  | 0.8614          | 0.75     | 0.7359 |
| 0.5123        | 18.0  | 441  | 1.0848          | 0.65     | 0.6306 |
| 0.4094        | 18.98 | 465  | 0.6748          | 0.7667   | 0.7680 |
| 0.3114        | 20.0  | 490  | 0.7406          | 0.75     | 0.7389 |
| 0.2668        | 20.98 | 514  | 0.8419          | 0.75     | 0.7424 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "wavlm-basic_n-f-n_8batch_5sec_0.0001lr_unfrozen", "results": []}]}
transformers pytorch tensorboard wavlm audio-classification generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wavlm #audio-classification #generated_from_trainer #endpoints_compatible #region-us 

wavlm-basic\_n-f-n\_8batch\_5sec\_0.0001lr\_unfrozen
====================================================


This model is a fine-tuned version of microsoft/wavlm-large on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.0704
* Accuracy: 0.7333
* F1: 0.7308


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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.003
* num\_epochs: 1000


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.003
* num\_epochs: 1000 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #wavlm #audio-classification #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.003
* num\_epochs: 1000 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #wavlm #audio-classification #generated_from_trainer #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.003
* num\_epochs: 1000### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3