| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: Project_NLP |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # Project_NLP |
| |
| 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: 0.5324 |
| - Wer: 0.3355 |
| |
| ## 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: 8 |
| - 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: 30 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:| |
| | 3.5697 | 1.0 | 500 | 2.1035 | 0.9979 | |
| | 0.8932 | 2.01 | 1000 | 0.5649 | 0.5621 | |
| | 0.4363 | 3.01 | 1500 | 0.4326 | 0.4612 | |
| | 0.3035 | 4.02 | 2000 | 0.4120 | 0.4191 | |
| | 0.2343 | 5.02 | 2500 | 0.4199 | 0.3985 | |
| | 0.1921 | 6.02 | 3000 | 0.4380 | 0.4043 | |
| | 0.1549 | 7.03 | 3500 | 0.4456 | 0.3925 | |
| | 0.1385 | 8.03 | 4000 | 0.4264 | 0.3871 | |
| | 0.1217 | 9.04 | 4500 | 0.4744 | 0.3774 | |
| | 0.1041 | 10.04 | 5000 | 0.4498 | 0.3745 | |
| | 0.0968 | 11.04 | 5500 | 0.4716 | 0.3628 | |
| | 0.0893 | 12.05 | 6000 | 0.4680 | 0.3764 | |
| | 0.078 | 13.05 | 6500 | 0.5100 | 0.3623 | |
| | 0.0704 | 14.06 | 7000 | 0.4893 | 0.3552 | |
| | 0.0659 | 15.06 | 7500 | 0.4956 | 0.3565 | |
| | 0.0578 | 16.06 | 8000 | 0.5450 | 0.3595 | |
| | 0.0563 | 17.07 | 8500 | 0.4891 | 0.3614 | |
| | 0.0557 | 18.07 | 9000 | 0.5307 | 0.3548 | |
| | 0.0447 | 19.08 | 9500 | 0.4923 | 0.3493 | |
| | 0.0456 | 20.08 | 10000 | 0.5156 | 0.3479 | |
| | 0.0407 | 21.08 | 10500 | 0.4979 | 0.3389 | |
| | 0.0354 | 22.09 | 11000 | 0.5549 | 0.3462 | |
| | 0.0322 | 23.09 | 11500 | 0.5601 | 0.3439 | |
| | 0.0342 | 24.1 | 12000 | 0.5131 | 0.3451 | |
| | 0.0276 | 25.1 | 12500 | 0.5206 | 0.3392 | |
| | 0.0245 | 26.1 | 13000 | 0.5337 | 0.3373 | |
| | 0.0226 | 27.11 | 13500 | 0.5311 | 0.3353 | |
| | 0.0229 | 28.11 | 14000 | 0.5375 | 0.3373 | |
| | 0.0225 | 29.12 | 14500 | 0.5324 | 0.3355 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.17.0 |
| - Pytorch 1.11.0+cu113 |
| - Datasets 1.18.3 |
| - Tokenizers 0.12.1 |
|
|
