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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: 0.1642 |
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- Wer: 0.1271 |
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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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| 1.0788 | 0.46 | 600 | 0.3546 | 0.4640 | |
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| 0.1732 | 0.92 | 1200 | 0.2307 | 0.3431 | |
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| 0.1247 | 1.38 | 1800 | 0.2097 | 0.2916 | |
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| 0.1092 | 1.84 | 2400 | 0.2121 | 0.2742 | |
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| 0.0896 | 2.3 | 3000 | 0.1817 | 0.2565 | |
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| 0.0776 | 2.76 | 3600 | 0.1715 | 0.2249 | |
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| 0.0695 | 3.22 | 4200 | 0.1684 | 0.2142 | |
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| 0.0581 | 3.68 | 4800 | 0.1667 | 0.2050 | |
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| 0.0521 | 4.14 | 5400 | 0.1629 | 0.1876 | |
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| 0.0426 | 4.6 | 6000 | 0.1553 | 0.1819 | |
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| 0.0389 | 5.06 | 6600 | 0.1485 | 0.1692 | |
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| 0.03 | 5.52 | 7200 | 0.1388 | 0.1667 | |
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| 0.03 | 5.98 | 7800 | 0.1441 | 0.1607 | |
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| 0.0208 | 6.44 | 8400 | 0.1444 | 0.1520 | |
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| 0.0209 | 6.9 | 9000 | 0.1339 | 0.1498 | |
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| 0.0145 | 7.36 | 9600 | 0.1418 | 0.1403 | |
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| 0.013 | 7.82 | 10200 | 0.1387 | 0.1351 | |
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| 0.0103 | 8.28 | 10800 | 0.1504 | 0.1321 | |
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| 0.0079 | 8.74 | 11400 | 0.1593 | 0.1331 | |
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| 0.0066 | 9.2 | 12000 | 0.1551 | 0.1222 | |
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| 0.004 | 9.66 | 12600 | 0.1642 | 0.1271 | |
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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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