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# bonito-wav2vec2-medium-demo

This model is a fine-tuned version of [yenpolin/bonito-wav2vec2-medium](https://huggingface.co/yenpolin/bonito-wav2vec2-medium) on the DNA_R9.4.1 - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1486
- Mean Acc: 0.0
- Median Acc: 0.0

## 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: 0.03
- train_batch_size: 96
- eval_batch_size: 192
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Acc | Median Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|
| 1.146         | 1.0   | 1042 | 1.1502          | 0.0      | 0.0        |
| 1.1432        | 2.0   | 2084 | 1.1491          | 0.0      | 0.0        |
| 1.1371        | 3.0   | 3126 | 1.1486          | 0.0      | 0.0        |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["automatic-speech-recognition", "dna_r9.4.1", "generated_from_trainer"], "model-index": [{"name": "bonito-wav2vec2-medium-demo", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition dna_r9.4.1 generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dna_r9.4.1 #generated_from_trainer #endpoints_compatible #region-us 

bonito-wav2vec2-medium-demo
===========================


This model is a fine-tuned version of yenpolin/bonito-wav2vec2-medium on the DNA\_R9.4.1 - NA dataset.
It achieves the following results on the evaluation set:


* Loss: 1.1486
* Mean Acc: 0.0
* Median Acc: 0.0


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: 0.03
* train\_batch\_size: 96
* eval\_batch\_size: 192
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.03
* train\_batch\_size: 96
* eval\_batch\_size: 192
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dna_r9.4.1 #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.03
* train\_batch\_size: 96
* eval\_batch\_size: 192
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dna_r9.4.1 #generated_from_trainer #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.03
* train\_batch\_size: 96
* eval\_batch\_size: 192
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.13.2