| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: 5sents_XLS-R_2_e-4 |
| 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. --> |
|
|
| # 5sents_XLS-R_2_e-4 |
| |
| This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.3629 |
| - Wer: 0.2063 |
| |
| ## 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: 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 |
| - training_steps: 400 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:-------:|:----:|:---------------:|:------:| |
| | 70.1025 | 99.89 | 200 | 23.5652 | 1.0 | |
| | 17.8988 | 199.89 | 400 | 10.4265 | 1.0 | |
| | 7.3246 | 299.89 | 600 | 4.0969 | 1.0 | |
| | 3.5815 | 399.89 | 800 | 2.4899 | 1.0 | |
| | 1.4553 | 499.89 | 1000 | 1.3636 | 0.7354 | |
| | 0.3355 | 599.89 | 1200 | 1.4502 | 0.3651 | |
| | 0.1232 | 699.89 | 1400 | 0.8715 | 0.3280 | |
| | 0.0615 | 799.89 | 1600 | 0.9018 | 0.3968 | |
| | 0.0372 | 899.89 | 1800 | 1.7271 | 0.4339 | |
| | 0.0247 | 999.89 | 2000 | 0.6459 | 0.2751 | |
| | 0.0166 | 1099.89 | 2200 | 0.4516 | 0.2540 | |
| | 0.0216 | 1199.89 | 2400 | 0.6955 | 0.2487 | |
| | 0.0093 | 1299.89 | 2600 | 1.1281 | 0.2646 | |
| | 0.0084 | 1399.89 | 2800 | 0.6150 | 0.1376 | |
| | 0.0076 | 1499.89 | 3000 | 1.1476 | 0.2646 | |
| | 0.0125 | 1599.89 | 3200 | 1.0682 | 0.2487 | |
| | 0.0096 | 1699.89 | 3400 | 0.8676 | 0.2487 | |
| | 0.0121 | 1799.89 | 3600 | 2.8241 | 0.2963 | |
| | 0.0107 | 1899.89 | 3800 | 0.3758 | 0.2381 | |
| | 0.0107 | 1999.89 | 4000 | 0.8708 | 0.2381 | |
| | 0.0051 | 2099.89 | 4200 | 0.8423 | 0.2804 | |
| | 0.0081 | 2199.89 | 4400 | 0.9489 | 0.2698 | |
| | 0.0044 | 2299.89 | 4600 | 0.8984 | 0.2857 | |
| | 0.0026 | 2399.89 | 4800 | 0.5836 | 0.2328 | |
| | 0.0169 | 2499.89 | 5000 | 0.9432 | 0.2434 | |
| | 0.0055 | 2599.89 | 5200 | 0.4225 | 0.2381 | |
| | 0.0033 | 2699.89 | 5400 | 1.1866 | 0.1693 | |
| | 0.0019 | 2799.89 | 5600 | 0.6218 | 0.1746 | |
| | 0.002 | 2899.89 | 5800 | 0.3831 | 0.1799 | |
| | 0.0026 | 2999.89 | 6000 | 0.6229 | 0.1323 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.26.1 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.11.0 |
| - Tokenizers 0.13.3 |
|
|
