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# wavlm-basic_s-r-5o_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: 0.9059
- Accuracy: 0.78
- F1: 0.7727

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.293         | 0.33  | 206  | 2.2895          | 0.21     | 0.1181 |
| 1.8057        | 0.66  | 412  | 1.6878          | 0.4633   | 0.4084 |
| 1.366         | 0.99  | 618  | 1.2287          | 0.6133   | 0.5503 |
| 1.0413        | 1.32  | 824  | 1.0882          | 0.6033   | 0.5617 |
| 0.8971        | 1.65  | 1030 | 0.9601          | 0.67     | 0.6569 |
| 0.613         | 1.98  | 1236 | 0.9091          | 0.7133   | 0.6854 |
| 0.4886        | 2.31  | 1442 | 0.6051          | 0.8567   | 0.8555 |
| 0.4439        | 2.64  | 1648 | 1.1049          | 0.7433   | 0.7378 |
| 0.3283        | 2.97  | 1854 | 1.0327          | 0.7233   | 0.7239 |
| 0.2892        | 3.3   | 2060 | 1.1137          | 0.7433   | 0.7429 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "wavlm-basic_s-r-5o_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\_s-r-5o\_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: 0.9059
* Accuracy: 0.78
* F1: 0.7727


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 2.12.0
* 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 2.12.0
* 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 2.12.0
* 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 2.12.0
* Tokenizers 0.13.3