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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: 0.1771 |
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- Wer: 0.1179 |
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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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| 1.1594 | 0.46 | 600 | 0.3721 | 0.4705 | |
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| 0.1751 | 0.92 | 1200 | 0.2652 | 0.3615 | |
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| 0.1269 | 1.38 | 1800 | 0.2069 | 0.2824 | |
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| 0.1113 | 1.84 | 2400 | 0.1867 | 0.2535 | |
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| 0.0904 | 2.3 | 3000 | 0.1907 | 0.2555 | |
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| 0.0783 | 2.76 | 3600 | 0.1740 | 0.2421 | |
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| 0.0691 | 3.22 | 4200 | 0.1860 | 0.2366 | |
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| 0.0588 | 3.68 | 4800 | 0.1696 | 0.2195 | |
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| 0.0541 | 4.14 | 5400 | 0.1560 | 0.1859 | |
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| 0.0421 | 4.6 | 6000 | 0.1812 | 0.1757 | |
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| 0.0385 | 5.06 | 6600 | 0.1643 | 0.1677 | |
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| 0.0305 | 5.52 | 7200 | 0.1457 | 0.1553 | |
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| 0.0309 | 5.98 | 7800 | 0.1494 | 0.1558 | |
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| 0.0214 | 6.44 | 8400 | 0.1516 | 0.1428 | |
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| 0.0216 | 6.9 | 9000 | 0.1409 | 0.1408 | |
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| 0.0146 | 7.36 | 9600 | 0.1524 | 0.1359 | |
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| 0.0133 | 7.82 | 10200 | 0.1494 | 0.1294 | |
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| 0.0103 | 8.28 | 10800 | 0.1600 | 0.1321 | |
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| 0.0079 | 8.74 | 11400 | 0.1658 | 0.1224 | |
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| 0.0065 | 9.2 | 12000 | 0.1644 | 0.1227 | |
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| 0.0043 | 9.66 | 12600 | 0.1771 | 0.1179 | |
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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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