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# RATS_clean

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3775
- Wer: 0.1324

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4025        | 0.42  | 1000  | 0.3019          | 0.1434 |
| 0.3939        | 0.85  | 2000  | 0.3044          | 0.1377 |
| 0.3808        | 1.27  | 3000  | 0.3053          | 0.1417 |
| 0.377         | 1.7   | 4000  | 0.3213          | 0.1414 |
| 0.3616        | 2.12  | 5000  | 0.2976          | 0.1449 |
| 0.3519        | 2.55  | 6000  | 0.3266          | 0.1482 |
| 0.3532        | 2.97  | 7000  | 0.3365          | 0.1455 |
| 0.3377        | 3.4   | 8000  | 0.3170          | 0.1409 |
| 0.3321        | 3.82  | 9000  | 0.3070          | 0.1398 |
| 0.3199        | 4.25  | 10000 | 0.3123          | 0.1366 |
| 0.322         | 4.67  | 11000 | 0.3166          | 0.1378 |
| 0.3085        | 5.1   | 12000 | 0.3492          | 0.1448 |
| 0.2954        | 5.52  | 13000 | 0.3173          | 0.1387 |
| 0.3003        | 5.95  | 14000 | 0.3341          | 0.1442 |
| 0.2863        | 6.37  | 15000 | 0.3124          | 0.1382 |
| 0.2887        | 6.8   | 16000 | 0.3273          | 0.1404 |
| 0.2777        | 7.22  | 17000 | 0.3291          | 0.1399 |
| 0.2728        | 7.65  | 18000 | 0.3326          | 0.1352 |
| 0.2726        | 8.07  | 19000 | 0.3443          | 0.1355 |
| 0.2519        | 8.5   | 20000 | 0.3448          | 0.1400 |
| 0.3256        | 8.92  | 21000 | 0.3274          | 0.1396 |
| 0.3174        | 9.35  | 22000 | 0.3210          | 0.1411 |
| 0.3075        | 9.77  | 23000 | 0.3319          | 0.1410 |
| 0.2982        | 10.2  | 24000 | 0.3463          | 0.1424 |
| 0.2927        | 10.62 | 25000 | 0.3445          | 0.1399 |
| 0.3027        | 11.05 | 26000 | 0.3488          | 0.1410 |
| 0.2835        | 11.47 | 27000 | 0.3639          | 0.1371 |
| 0.2767        | 11.89 | 28000 | 0.3467          | 0.1391 |
| 0.2792        | 12.32 | 29000 | 0.3459          | 0.1358 |
| 0.2701        | 12.74 | 30000 | 0.3425          | 0.1351 |
| 0.269         | 13.17 | 31000 | 0.3790          | 0.1421 |
| 0.2567        | 13.59 | 32000 | 0.3613          | 0.1352 |
| 0.2626        | 14.02 | 33000 | 0.3586          | 0.1396 |
| 0.2436        | 14.44 | 34000 | 0.3694          | 0.1369 |
| 0.2557        | 14.87 | 35000 | 0.3715          | 0.1321 |
| 0.2468        | 15.29 | 36000 | 0.3970          | 0.1348 |
| 0.2463        | 15.72 | 37000 | 0.3675          | 0.1304 |
| 0.2362        | 16.14 | 38000 | 0.3690          | 0.1377 |
| 0.2388        | 16.57 | 39000 | 0.3775          | 0.1310 |
| 0.2314        | 16.99 | 40000 | 0.3601          | 0.1326 |
| 0.2315        | 17.42 | 41000 | 0.3633          | 0.1322 |
| 0.2334        | 17.84 | 42000 | 0.3794          | 0.1356 |
| 0.2255        | 18.27 | 43000 | 0.3670          | 0.1316 |
| 0.2222        | 18.69 | 44000 | 0.3778          | 0.1341 |
| 0.225         | 19.12 | 45000 | 0.3708          | 0.1331 |
| 0.2209        | 19.54 | 46000 | 0.3807          | 0.1332 |
| 0.2216        | 19.97 | 47000 | 0.3775          | 0.1324 |


### Framework versions

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

{"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "RATS_clean", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 

RATS\_clean
===========


This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3775
* Wer: 0.1324


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: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### 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: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


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


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


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


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


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