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# wav2vec2-300m-ft-soft-skill

This model is a fine-tuned version of [glob-asr/xls-r-es-test-lm](https://huggingface.co/glob-asr/xls-r-es-test-lm) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7447
- Accuracy: 0.6827
- F1 Micro: 0.3514
- F1 Macro: 0.6827
- Precision Micro: 0.6827

## 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: 8
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | Precision Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|
| 0.823         | 0.51  | 100  | 0.6821          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.7122        | 1.02  | 200  | 0.6767          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6706        | 1.52  | 300  | 0.6768          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.7096        | 2.03  | 400  | 0.6791          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6909        | 2.54  | 500  | 0.6780          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6861        | 3.05  | 600  | 0.6779          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6842        | 3.55  | 700  | 0.6773          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6887        | 4.06  | 800  | 0.6764          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6766        | 4.57  | 900  | 0.6803          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6964        | 5.08  | 1000 | 0.6819          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6515        | 5.58  | 1100 | 0.6788          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6608        | 6.09  | 1200 | 0.6864          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6171        | 6.6   | 1300 | 0.6980          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6292        | 7.11  | 1400 | 0.7172          | 0.7386   | 0.3119   | 0.7386   | 0.7386          |
| 0.6015        | 7.61  | 1500 | 0.6988          | 0.7462   | 0.3212   | 0.7462   | 0.7462          |
| 0.6236        | 8.12  | 1600 | 0.7493          | 0.6954   | 0.3432   | 0.6954   | 0.6954          |
| 0.5643        | 8.63  | 1700 | 0.7250          | 0.7107   | 0.3466   | 0.7107   | 0.7107          |
| 0.6134        | 9.14  | 1800 | 0.7561          | 0.6751   | 0.3565   | 0.6751   | 0.6751          |
| 0.5642        | 9.64  | 1900 | 0.7447          | 0.6827   | 0.3514   | 0.6827   | 0.6827          |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.8.1+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-300m-ft-soft-skill", "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 

wav2vec2-300m-ft-soft-skill
===========================


This model is a fine-tuned version of glob-asr/xls-r-es-test-lm on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7447
* Accuracy: 0.6827
* F1 Micro: 0.3514
* F1 Macro: 0.6827
* Precision Micro: 0.6827


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: 8
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.21.3
* Pytorch 1.8.1+cu111
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.3
* Pytorch 1.8.1+cu111
* Datasets 2.4.0
* Tokenizers 0.12.1
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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.3
* Pytorch 1.8.1+cu111
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: 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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.21.3
* Pytorch 1.8.1+cu111
* Datasets 2.4.0
* Tokenizers 0.12.1