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--- |
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base_model: facebook/w2v-bert-2.0 |
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license: mit |
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metrics: |
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- wer |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: w2v-bert-2.0-nonstudio_and_studioRecords_final |
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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_final |
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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: 0.1772 |
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- Wer: 0.1266 |
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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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| 1.055 | 0.4601 | 600 | 0.3683 | 0.4608 | |
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| 0.1734 | 0.9202 | 1200 | 0.2620 | 0.3546 | |
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| 0.1242 | 1.3804 | 1800 | 0.2115 | 0.3018 | |
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| 0.1075 | 1.8405 | 2400 | 0.2004 | 0.2889 | |
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| 0.0888 | 2.3006 | 3000 | 0.1870 | 0.2573 | |
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| 0.078 | 2.7607 | 3600 | 0.1724 | 0.2267 | |
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| 0.0664 | 3.2209 | 4200 | 0.1572 | 0.2244 | |
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| 0.0576 | 3.6810 | 4800 | 0.1746 | 0.2217 | |
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| 0.0522 | 4.1411 | 5400 | 0.1643 | 0.1796 | |
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| 0.0415 | 4.6012 | 6000 | 0.1781 | 0.1851 | |
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| 0.0398 | 5.0613 | 6600 | 0.1670 | 0.1714 | |
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| 0.0301 | 5.5215 | 7200 | 0.1531 | 0.1617 | |
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| 0.0296 | 5.9816 | 7800 | 0.1463 | 0.1590 | |
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| 0.0211 | 6.4417 | 8400 | 0.1566 | 0.1473 | |
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| 0.0206 | 6.9018 | 9000 | 0.1423 | 0.1468 | |
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| 0.0147 | 7.3620 | 9600 | 0.1443 | 0.1413 | |
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| 0.0136 | 7.8221 | 10200 | 0.1539 | 0.1418 | |
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| 0.0105 | 8.2822 | 10800 | 0.1611 | 0.1383 | |
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| 0.0079 | 8.7423 | 11400 | 0.1761 | 0.1351 | |
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| 0.0063 | 9.2025 | 12000 | 0.1814 | 0.1304 | |
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| 0.0043 | 9.6626 | 12600 | 0.1772 | 0.1266 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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