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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: hubert-base-libri-pruning-v1 |
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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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# hubert-base-libri-pruning-v1 |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: -7634.6143 |
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- Wer: 0.4250 |
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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: 0.00015 |
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- train_batch_size: 64 |
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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: 3000 |
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- num_epochs: 30 |
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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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| -14.3776 | 1.12 | 500 | -47.0374 | 0.5235 | |
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| -106.5986 | 2.24 | 1000 | -189.7623 | 0.4825 | |
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| -291.2003 | 3.36 | 1500 | -428.0651 | 0.4726 | |
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| -567.486 | 4.48 | 2000 | -761.7246 | 0.4628 | |
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| -934.1564 | 5.61 | 2500 | -1190.6781 | 0.4580 | |
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| -1396.4066 | 6.73 | 3000 | -1714.8335 | 0.4487 | |
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| -1930.5184 | 7.85 | 3500 | -2271.9685 | 0.4422 | |
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| -2452.421 | 8.97 | 4000 | -2802.5127 | 0.4418 | |
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| -2953.0952 | 10.09 | 4500 | -3304.3562 | 0.4386 | |
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| -3427.7243 | 11.21 | 5000 | -3779.5505 | 0.4357 | |
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| -3879.0445 | 12.33 | 5500 | -4227.1289 | 0.4339 | |
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| -4302.3395 | 13.45 | 6000 | -4647.1260 | 0.4311 | |
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| -4680.295 | 14.57 | 6500 | -5039.4692 | 0.4283 | |
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| -5054.8855 | 15.7 | 7000 | -5404.1592 | 0.4306 | |
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| -5377.8435 | 16.82 | 7500 | -5741.4082 | 0.4286 | |
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| -5688.8665 | 17.94 | 8000 | -6051.0688 | 0.4290 | |
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| -5990.955 | 19.06 | 8500 | -6333.1387 | 0.4284 | |
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| -6252.404 | 20.18 | 9000 | -6587.1460 | 0.4257 | |
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| -6481.961 | 21.3 | 9500 | -6814.2788 | 0.4268 | |
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| -6695.5835 | 22.42 | 10000 | -7013.8809 | 0.4256 | |
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| -6859.0875 | 23.54 | 10500 | -7185.9956 | 0.4255 | |
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| -7015.5155 | 24.66 | 11000 | -7330.6577 | 0.4271 | |
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| -7170.5215 | 25.78 | 11500 | -7447.6372 | 0.4256 | |
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| -7244.894 | 26.91 | 12000 | -7537.4111 | 0.4243 | |
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| -7320.932 | 28.03 | 12500 | -7599.7690 | 0.4248 | |
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| -7366.4105 | 29.15 | 13000 | -7634.6143 | 0.4250 | |
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### Framework versions |
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- Transformers 4.30.0.dev0 |
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- Pytorch 2.0.1 |
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- Datasets 2.12.1.dev0 |
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- Tokenizers 0.13.3 |
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