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# wav2vec2-base-digit-mask-ft

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0054
- Accuracy: 0.9989
- F1: 0.9989

## 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: 1e-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: 3

### Training results

| Training Loss | Epoch | Step  | Accuracy | F1     | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:------:|:---------------:|
| 0.0083        | 1.0   | 14264 | 0.9989   | 0.9989 | 0.0054          |
| 0.0002        | 2.0   | 28528 | 0.9989   | 0.9989 | 0.0057          |
| 0.0003        | 3.0   | 42792 | 0.9989   | 0.9989 | 0.0065          |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mazkooleg/digit_mask_augmented_raw"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "wav2vec2-base-digit-mask-ft", "results": []}]}
transformers pytorch wav2vec2 audio-classification generated_from_trainer dataset:mazkooleg/digit_mask_augmented_raw license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #dataset-mazkooleg/digit_mask_augmented_raw #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-base-digit-mask-ft
===========================


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


* Loss: 0.0054
* Accuracy: 0.9989
* F1: 0.9989


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: 1e-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: 3


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cpu
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 3 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cpu
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #dataset-mazkooleg/digit_mask_augmented_raw #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 3 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cpu
* Datasets 2.12.0
* Tokenizers 0.13.3