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--- |
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license: mit |
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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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base_model: facebook/w2v-bert-2.0 |
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model-index: |
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- name: w2v-bert-2.0-nonstudio_and_studioRecords |
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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-bert-2.0-nonstudio_and_studioRecords |
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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: 0.1679 |
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- Wer: 0.1214 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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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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| 1.1162 | 0.46 | 600 | 0.4548 | 0.5036 | |
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| 0.1745 | 0.92 | 1200 | 0.2634 | 0.3735 | |
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| 0.1268 | 1.38 | 1800 | 0.2148 | 0.2951 | |
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| 0.1106 | 1.84 | 2400 | 0.2106 | 0.2677 | |
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| 0.0917 | 2.3 | 3000 | 0.1963 | 0.2466 | |
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| 0.0794 | 2.76 | 3600 | 0.1789 | 0.2441 | |
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| 0.0688 | 3.22 | 4200 | 0.1911 | 0.2371 | |
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| 0.0586 | 3.68 | 4800 | 0.1774 | 0.2013 | |
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| 0.0534 | 4.14 | 5400 | 0.1567 | 0.1876 | |
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| 0.0417 | 4.6 | 6000 | 0.1733 | 0.1804 | |
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| 0.0396 | 5.06 | 6600 | 0.1537 | 0.1595 | |
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| 0.0302 | 5.52 | 7200 | 0.1559 | 0.1545 | |
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| 0.03 | 5.98 | 7800 | 0.1482 | 0.1560 | |
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| 0.0214 | 6.44 | 8400 | 0.1554 | 0.1622 | |
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| 0.0209 | 6.9 | 9000 | 0.1464 | 0.1433 | |
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| 0.015 | 7.36 | 9600 | 0.1626 | 0.1386 | |
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| 0.0129 | 7.82 | 10200 | 0.1601 | 0.1331 | |
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| 0.0105 | 8.28 | 10800 | 0.1651 | 0.1373 | |
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| 0.0078 | 8.74 | 11400 | 0.1600 | 0.1237 | |
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| 0.0061 | 9.2 | 12000 | 0.1694 | 0.1269 | |
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| 0.004 | 9.66 | 12600 | 0.1679 | 0.1214 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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