| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: finetuning11 |
| 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. --> |
|
|
| # finetuning11 |
|
|
| 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: nan |
| - Wer: 1.0 |
|
|
| ## 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: 0.00024 |
| - train_batch_size: 1 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 2 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 800 |
| - num_epochs: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:-----:|:----:|:---------------:|:---:| |
| | 0.0 | 0.31 | 500 | nan | 1.0 | |
| | 0.0 | 0.61 | 1000 | nan | 1.0 | |
| | 0.0 | 0.92 | 1500 | nan | 1.0 | |
| | 0.0 | 1.23 | 2000 | nan | 1.0 | |
| | 0.0 | 1.54 | 2500 | nan | 1.0 | |
| | 0.0 | 1.84 | 3000 | nan | 1.0 | |
| | 0.0 | 2.15 | 3500 | nan | 1.0 | |
| | 0.0 | 2.46 | 4000 | nan | 1.0 | |
| | 0.0 | 2.77 | 4500 | nan | 1.0 | |
| | 0.0 | 3.07 | 5000 | nan | 1.0 | |
| | 0.0 | 3.38 | 5500 | nan | 1.0 | |
| | 0.0 | 3.69 | 6000 | nan | 1.0 | |
| | 0.0 | 4.0 | 6500 | nan | 1.0 | |
| | 0.0 | 4.3 | 7000 | nan | 1.0 | |
| | 0.0 | 4.61 | 7500 | nan | 1.0 | |
| | 0.0 | 4.92 | 8000 | nan | 1.0 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.26.1 |
| - Pytorch 1.13.1+cu116 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.2 |
|
|
