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# wavlm-large-finetuned-iemocap2

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: 1.0935
- Accuracy: 0.5335
- F1: 0.5005

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3826        | 0.98  | 25   | 1.3815          | 0.2502   | 0.1003 |
| 1.3263        | 1.98  | 50   | 1.3663          | 0.2502   | 0.1002 |
| 1.2563        | 2.98  | 75   | 1.2589          | 0.3870   | 0.3051 |
| 1.1869        | 3.98  | 100  | 1.2042          | 0.3977   | 0.3428 |
| 1.1291        | 4.98  | 125  | 1.1768          | 0.4539   | 0.4557 |
| 1.1171        | 5.98  | 150  | 1.1425          | 0.4888   | 0.4799 |
| 1.0811        | 6.98  | 175  | 1.1316          | 0.4956   | 0.4851 |
| 1.0627        | 7.98  | 200  | 1.1241          | 0.5044   | 0.4859 |
| 1.079         | 8.98  | 225  | 1.1026          | 0.5228   | 0.5031 |
| 1.0294        | 9.98  | 250  | 1.1018          | 0.5199   | 0.4959 |
| 1.0088        | 10.98 | 275  | 1.0903          | 0.5325   | 0.5046 |
| 1.0217        | 11.98 | 300  | 1.0966          | 0.5296   | 0.5015 |
| 1.0034        | 12.98 | 325  | 1.1012          | 0.5296   | 0.4990 |
| 1.0024        | 13.98 | 350  | 1.0832          | 0.5393   | 0.5127 |
| 1.0047        | 14.98 | 375  | 1.0902          | 0.5315   | 0.4986 |
| 0.9436        | 15.98 | 400  | 1.0896          | 0.5373   | 0.5085 |
| 0.9584        | 16.98 | 425  | 1.0859          | 0.5412   | 0.5114 |
| 0.9859        | 17.98 | 450  | 1.0865          | 0.5412   | 0.5120 |
| 0.9679        | 18.98 | 475  | 1.0926          | 0.5335   | 0.4999 |
| 0.9468        | 19.98 | 500  | 1.0935          | 0.5335   | 0.5005 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "wavlm-large-finetuned-iemocap2", "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-large-finetuned-iemocap2
==============================


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: 1.0935
* Accuracy: 0.5335
* F1: 0.5005


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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
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: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
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: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2