
# wav2vec2-base-finetuned-sentiment-mesd

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [MESD](https://huggingface.co/hackathon-pln-es/MESD) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5729
- Accuracy: 0.8308

## Model description

This model was trained to classify underlying sentiment of Spanish audio/speech.

## 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: 1.25e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 7    | 0.5729          | 0.8308   |
| No log        | 2.0   | 14   | 0.6577          | 0.8      |
| 0.1602        | 3.0   | 21   | 0.7055          | 0.8      |
| 0.1602        | 4.0   | 28   | 0.8696          | 0.7615   |
| 0.1602        | 5.0   | 35   | 0.6807          | 0.7923   |
| 0.1711        | 6.0   | 42   | 0.7303          | 0.7923   |
| 0.1711        | 7.0   | 49   | 0.7028          | 0.8077   |
| 0.1711        | 8.0   | 56   | 0.7368          | 0.8      |
| 0.1608        | 9.0   | 63   | 0.7190          | 0.7923   |
| 0.1608        | 10.0  | 70   | 0.6913          | 0.8077   |
| 0.1608        | 11.0  | 77   | 0.7047          | 0.8077   |
| 0.1753        | 12.0  | 84   | 0.6801          | 0.8      |
| 0.1753        | 13.0  | 91   | 0.7208          | 0.7769   |
| 0.1753        | 14.0  | 98   | 0.7458          | 0.7846   |
| 0.203         | 15.0  | 105  | 0.6494          | 0.8077   |
| 0.203         | 16.0  | 112  | 0.6256          | 0.8231   |
| 0.203         | 17.0  | 119  | 0.6788          | 0.8      |
| 0.1919        | 18.0  | 126  | 0.6757          | 0.7846   |
| 0.1919        | 19.0  | 133  | 0.6859          | 0.7846   |
| 0.1641        | 20.0  | 140  | 0.6832          | 0.7846   |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "wav2vec2-base-finetuned-sentiment-mesd", "results": []}]}
transformers pytorch tensorboard wav2vec2 audio-classification generated_from_trainer base_model:facebook/wav2vec2-base license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #has_space #region-us 

wav2vec2-base-finetuned-sentiment-mesd
======================================


This model is a fine-tuned version of facebook/wav2vec2-base on the MESD dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5729
* Accuracy: 0.8308


Model description
-----------------


This model was trained to classify underlying sentiment of Spanish audio/speech.


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: 1.25e-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 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1.25e-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 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1.25e-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 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1.25e-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 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.10.3