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--- |
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library_name: transformers |
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license: mit |
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base_model: facebook/w2v-bert-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: w2v-V2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# w2v-V2 |
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This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1706 |
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- Wer: 0.1496 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- training_steps: 5000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:------:|:----:|:---------------:|:------:| |
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| 0.3589 | 0.1049 | 300 | 0.2921 | 0.2762 | |
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| 0.3512 | 0.2099 | 600 | 0.2855 | 0.2767 | |
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| 0.2998 | 0.3148 | 900 | 0.2872 | 0.2550 | |
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| 0.3419 | 0.4197 | 1200 | 0.2641 | 0.2620 | |
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| 0.2757 | 0.5247 | 1500 | 0.2633 | 0.2332 | |
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| 0.2827 | 0.6296 | 1800 | 0.2473 | 0.2090 | |
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| 0.265 | 0.7345 | 2100 | 0.2304 | 0.2226 | |
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| 0.2985 | 0.8395 | 2400 | 0.2266 | 0.2109 | |
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| 0.2555 | 0.9444 | 2700 | 0.2279 | 0.1891 | |
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| 0.255 | 1.0493 | 3000 | 0.2129 | 0.1927 | |
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| 0.2194 | 1.1542 | 3300 | 0.1991 | 0.1821 | |
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| 0.172 | 1.2592 | 3600 | 0.1963 | 0.1710 | |
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| 0.2018 | 1.3641 | 3900 | 0.1860 | 0.1724 | |
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| 0.2098 | 1.4690 | 4200 | 0.1783 | 0.1717 | |
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| 0.1996 | 1.5740 | 4500 | 0.1709 | 0.1563 | |
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| 0.1926 | 1.6789 | 4800 | 0.1706 | 0.1496 | |
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### Framework versions |
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- Transformers 4.51.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.3.1 |
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- Tokenizers 0.21.0 |
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