| --- |
| library_name: transformers |
| license: mit |
| base_model: facebook/w2v-bert-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: w2vbert-ctc-salt |
| 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. --> |
|
|
| # w2vbert-ctc-salt |
|
|
| This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3020 |
| - Wer: 0.3905 |
| - Cer: 0.0840 |
|
|
| ## 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: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 16 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 0.1 |
| - training_steps: 15000 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |
| |:-------------:|:------:|:-----:|:------:|:---------------:|:------:| |
| | 5.9489 | 0.2076 | 1500 | 1.0 | 3.0230 | 1.0 | |
| | 1.5319 | 0.4152 | 3000 | 0.5960 | 0.5589 | 0.1293 | |
| | 1.1602 | 0.6228 | 4500 | 0.4309 | 0.4809 | 0.1054 | |
| | 1.0148 | 0.8304 | 6000 | 0.3715 | 0.4499 | 0.0974 | |
| | 0.9507 | 1.0381 | 7500 | 0.3443 | 0.4274 | 0.0927 | |
| | 0.9469 | 1.2457 | 9000 | 0.3220 | 0.4031 | 0.0876 | |
| | 0.8564 | 1.4533 | 10500 | 0.3134 | 0.3995 | 0.0864 | |
| | 0.8318 | 1.6609 | 12000 | 0.3061 | 0.3951 | 0.0848 | |
| | 0.8707 | 1.8685 | 13500 | 0.3033 | 0.3904 | 0.0841 | |
| | 0.9274 | 2.0761 | 15000 | 0.3020 | 0.3905 | 0.0840 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.2.0 |
| - Pytorch 2.10.0+cu130 |
| - Datasets 4.6.0 |
| - Tokenizers 0.22.2 |
| |
