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# wav2vec2-large-finetuned-iemocap2

This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2460
- Accuracy: 0.5209
- F1: 0.5049

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3574        | 0.98  | 25   | 1.4694          | 0.2502   | 0.1002 |
| 1.1919        | 1.98  | 50   | 1.3444          | 0.3754   | 0.3304 |
| 1.1571        | 2.98  | 75   | 1.2644          | 0.4064   | 0.3649 |
| 1.1165        | 3.98  | 100  | 1.1895          | 0.4762   | 0.4223 |
| 1.0498        | 4.98  | 125  | 1.1373          | 0.5053   | 0.4920 |
| 1.0147        | 5.98  | 150  | 1.1089          | 0.5131   | 0.4763 |
| 1.0163        | 6.98  | 175  | 1.1595          | 0.5092   | 0.4651 |
| 0.9711        | 7.98  | 200  | 1.1298          | 0.5179   | 0.4759 |
| 0.9599        | 8.98  | 225  | 1.1460          | 0.5199   | 0.4831 |
| 0.9042        | 9.98  | 250  | 1.1191          | 0.5500   | 0.5307 |
| 0.8734        | 10.98 | 275  | 1.2103          | 0.5364   | 0.4935 |
| 0.8876        | 11.98 | 300  | 1.1837          | 0.5228   | 0.4912 |
| 0.8369        | 12.98 | 325  | 1.2009          | 0.5296   | 0.4927 |
| 0.8357        | 13.98 | 350  | 1.2144          | 0.5238   | 0.5054 |
| 0.8314        | 14.98 | 375  | 1.1866          | 0.5335   | 0.5180 |
| 0.761         | 15.98 | 400  | 1.2145          | 0.5451   | 0.5317 |
| 0.7723        | 16.98 | 425  | 1.2033          | 0.5276   | 0.5073 |
| 0.7775        | 17.98 | 450  | 1.2841          | 0.5228   | 0.4986 |
| 0.7735        | 18.98 | 475  | 1.2249          | 0.5393   | 0.5253 |
| 0.726         | 19.98 | 500  | 1.2460          | 0.5209   | 0.5049 |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "wav2vec2-large-finetuned-iemocap2", "results": []}]}
transformers pytorch tensorboard wav2vec2 audio-classification generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-large-finetuned-iemocap2
=================================


This model is a fine-tuned version of facebook/wav2vec2-large on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.2460
* Accuracy: 0.5209
* F1: 0.5049


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 #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #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 #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #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