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# wav2vec2-base-finetuned-mednames1

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5263
- Accuracy: 0.9727

## 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: 5
- eval_batch_size: 5
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2881        | 1.0   | 22   | 2.2690          | 0.2273   |
| 2.2234        | 2.0   | 44   | 2.1631          | 0.2273   |
| 1.9756        | 3.0   | 66   | 1.9277          | 0.5182   |
| 1.7191        | 4.0   | 88   | 1.6293          | 0.6818   |
| 1.4663        | 5.0   | 110  | 1.3919          | 0.7545   |
| 1.27          | 6.0   | 132  | 1.1689          | 0.8182   |
| 1.1112        | 7.0   | 154  | 1.0144          | 0.8364   |
| 0.9623        | 8.0   | 176  | 0.9137          | 0.8545   |
| 0.8764        | 9.0   | 198  | 0.7901          | 0.8909   |
| 0.7776        | 10.0  | 220  | 0.7229          | 0.8727   |
| 0.7266        | 11.0  | 242  | 0.6335          | 0.9      |
| 0.6379        | 12.0  | 264  | 0.5848          | 0.9636   |
| 0.6121        | 13.0  | 286  | 0.5509          | 0.9273   |
| 0.5732        | 14.0  | 308  | 0.5263          | 0.9727   |
| 0.5579        | 15.0  | 330  | 0.5221          | 0.9727   |


### Framework versions

- Transformers 4.27.2
- Pytorch 1.11.0+cu102
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-mednames1", "results": []}]}
transformers pytorch tensorboard wav2vec2 audio-classification generated_from_trainer dataset:audiofolder license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #license-apache-2.0 #endpoints_compatible #has_space #region-us 

wav2vec2-base-finetuned-mednames1
=================================


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


* Loss: 0.5263
* Accuracy: 0.9727


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: 5
* eval\_batch\_size: 5
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.27.2
* Pytorch 1.11.0+cu102
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 5
* eval\_batch\_size: 5
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.11.0+cu102
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 5
* eval\_batch\_size: 5
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.11.0+cu102
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #license-apache-2.0 #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 5
* eval\_batch\_size: 5
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15### Training results### Framework versions


* Transformers 4.27.2
* Pytorch 1.11.0+cu102
* Datasets 2.10.1
* Tokenizers 0.13.2