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--- |
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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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model-index: |
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- name: tun_wav2vec8 |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/myriam-charfeddine5/huggingface/runs/w8fp109b) |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/myriam-charfeddine5/huggingface/runs/w8fp109b) |
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# tun_wav2vec8 |
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This model was trained from scratch on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2858 |
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- Wer: 0.5831 |
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- Cer: 0.1539 |
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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: 0.0001 |
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- train_batch_size: 10 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 80 |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| 2.2416 | 5.0 | 300 | 1.6570 | 0.9561 | 0.3341 | |
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| 0.6397 | 10.0 | 600 | 1.0742 | 0.8652 | 0.3005 | |
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| 0.4434 | 15.0 | 900 | 1.2856 | 0.7743 | 0.2411 | |
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| 0.3084 | 20.0 | 1200 | 1.0868 | 0.7335 | 0.2106 | |
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| 0.2708 | 25.0 | 1500 | 0.9412 | 0.7367 | 0.1960 | |
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| 0.2519 | 30.0 | 1800 | 0.8857 | 0.6959 | 0.1863 | |
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| 0.1833 | 35.0 | 2100 | 1.2220 | 0.6740 | 0.1856 | |
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| 0.1354 | 40.0 | 2400 | 1.1682 | 0.6520 | 0.1786 | |
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| 0.1363 | 45.0 | 2700 | 1.1745 | 0.6865 | 0.1794 | |
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| 0.129 | 50.0 | 3000 | 1.0153 | 0.6426 | 0.1736 | |
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| 0.1036 | 55.0 | 3300 | 1.1114 | 0.6332 | 0.1705 | |
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| 0.1011 | 60.0 | 3600 | 1.4662 | 0.6238 | 0.1794 | |
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| 0.0902 | 65.0 | 3900 | 1.3797 | 0.6426 | 0.1779 | |
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| 0.074 | 70.0 | 4200 | 1.4517 | 0.6207 | 0.1813 | |
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| 0.0648 | 75.0 | 4500 | 1.2976 | 0.6207 | 0.1694 | |
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| 0.0591 | 80.0 | 4800 | 1.3030 | 0.5987 | 0.1690 | |
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| 0.0622 | 85.0 | 5100 | 1.2847 | 0.5925 | 0.1636 | |
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| 0.0639 | 90.0 | 5400 | 1.3230 | 0.5925 | 0.1659 | |
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| 0.0816 | 95.0 | 5700 | 1.2766 | 0.5925 | 0.1582 | |
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| 0.0444 | 100.0 | 6000 | 1.2858 | 0.5831 | 0.1539 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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