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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: 0.1651 |
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- Wer: 0.1279 |
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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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| 1.4399 | 0.46 | 600 | 0.3709 | 0.4586 | |
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| 0.1715 | 0.92 | 1200 | 0.2427 | 0.3379 | |
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| 0.1226 | 1.38 | 1800 | 0.2216 | 0.2919 | |
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| 0.1073 | 1.84 | 2400 | 0.1992 | 0.2672 | |
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| 0.0888 | 2.3 | 3000 | 0.1835 | 0.2506 | |
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| 0.0781 | 2.76 | 3600 | 0.1768 | 0.2491 | |
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| 0.0671 | 3.22 | 4200 | 0.1702 | 0.2309 | |
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| 0.0575 | 3.68 | 4800 | 0.1784 | 0.2202 | |
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| 0.0531 | 4.14 | 5400 | 0.1690 | 0.1881 | |
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| 0.0421 | 4.6 | 6000 | 0.1707 | 0.1876 | |
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| 0.0398 | 5.06 | 6600 | 0.1672 | 0.1759 | |
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| 0.0305 | 5.52 | 7200 | 0.1516 | 0.1575 | |
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| 0.0306 | 5.98 | 7800 | 0.1576 | 0.1682 | |
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| 0.0219 | 6.44 | 8400 | 0.1516 | 0.1610 | |
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| 0.021 | 6.9 | 9000 | 0.1418 | 0.1493 | |
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| 0.0151 | 7.36 | 9600 | 0.1343 | 0.1359 | |
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| 0.0133 | 7.82 | 10200 | 0.1410 | 0.1433 | |
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| 0.0103 | 8.28 | 10800 | 0.1564 | 0.1386 | |
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| 0.0084 | 8.74 | 11400 | 0.1546 | 0.1276 | |
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| 0.0067 | 9.2 | 12000 | 0.1622 | 0.1244 | |
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| 0.0043 | 9.66 | 12600 | 0.1651 | 0.1279 | |
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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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