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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-large-xlsr-53 |
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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: xlsr-big-kannn |
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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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# xlsr-big-kannn |
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0000 |
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- Wer: 0.0510 |
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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: 0.0004 |
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- train_batch_size: 8 |
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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: 16 |
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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: 132 |
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- num_epochs: 100 |
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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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| 2.0631 | 1.9704 | 200 | 0.6852 | 0.5241 | |
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| 0.3968 | 3.9409 | 400 | 0.0531 | 0.1099 | |
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| 0.1256 | 5.9113 | 600 | 0.0184 | 0.0633 | |
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| 0.0844 | 7.8818 | 800 | 0.0339 | 0.0643 | |
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| 0.0669 | 9.8522 | 1000 | 0.0070 | 0.0571 | |
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| 0.05 | 11.8227 | 1200 | 0.0029 | 0.0545 | |
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| 0.0467 | 13.7931 | 1400 | 0.0049 | 0.0531 | |
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| 0.0369 | 15.7635 | 1600 | 0.0051 | 0.0593 | |
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| 0.0267 | 17.7340 | 1800 | 0.0016 | 0.0529 | |
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| 0.0297 | 19.7044 | 2000 | 0.0010 | 0.0581 | |
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| 0.0276 | 21.6749 | 2200 | 0.0041 | 0.0579 | |
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| 0.0211 | 23.6453 | 2400 | 0.0020 | 0.0525 | |
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| 0.0324 | 25.6158 | 2600 | 0.0091 | 0.0551 | |
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| 0.0223 | 27.5862 | 2800 | 0.0013 | 0.0539 | |
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| 0.0182 | 29.5567 | 3000 | 0.0026 | 0.0551 | |
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| 0.0167 | 31.5271 | 3200 | 0.0010 | 0.0551 | |
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| 0.0173 | 33.4975 | 3400 | 0.0007 | 0.0518 | |
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| 0.0178 | 35.4680 | 3600 | 0.0012 | 0.0510 | |
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| 0.0172 | 37.4384 | 3800 | 0.0008 | 0.0514 | |
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| 0.0138 | 39.4089 | 4000 | 0.0006 | 0.0504 | |
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| 0.0122 | 41.3793 | 4200 | 0.0002 | 0.0512 | |
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| 0.0134 | 43.3498 | 4400 | 0.0003 | 0.0514 | |
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| 0.0129 | 45.3202 | 4600 | 0.0003 | 0.0512 | |
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| 0.0075 | 47.2906 | 4800 | 0.0001 | 0.0512 | |
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| 0.0067 | 49.2611 | 5000 | 0.0001 | 0.0545 | |
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| 0.0083 | 51.2315 | 5200 | 0.0003 | 0.0527 | |
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| 0.0067 | 53.2020 | 5400 | 0.0001 | 0.0525 | |
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| 0.0065 | 55.1724 | 5600 | 0.0004 | 0.0523 | |
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| 0.0073 | 57.1429 | 5800 | 0.0000 | 0.0504 | |
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| 0.0051 | 59.1133 | 6000 | 0.0001 | 0.0510 | |
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| 0.0077 | 61.0837 | 6200 | 0.0006 | 0.0510 | |
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| 0.0069 | 63.0542 | 6400 | 0.0006 | 0.0510 | |
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| 0.0058 | 65.0246 | 6600 | 0.0001 | 0.0506 | |
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| 0.0044 | 66.9951 | 6800 | 0.0003 | 0.0508 | |
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| 0.0046 | 68.9655 | 7000 | 0.0000 | 0.0506 | |
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| 0.0049 | 70.9360 | 7200 | 0.0000 | 0.0508 | |
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| 0.0035 | 72.9064 | 7400 | 0.0001 | 0.0520 | |
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| 0.0022 | 74.8768 | 7600 | 0.0000 | 0.0527 | |
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| 0.0039 | 76.8473 | 7800 | 0.0000 | 0.0518 | |
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| 0.0033 | 78.8177 | 8000 | 0.0000 | 0.0516 | |
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| 0.0028 | 80.7882 | 8200 | 0.0000 | 0.0506 | |
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| 0.0024 | 82.7586 | 8400 | 0.0000 | 0.0510 | |
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| 0.0016 | 84.7291 | 8600 | 0.0000 | 0.0508 | |
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| 0.0017 | 86.6995 | 8800 | 0.0000 | 0.0506 | |
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| 0.002 | 88.6700 | 9000 | 0.0000 | 0.0512 | |
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| 0.0021 | 90.6404 | 9200 | 0.0001 | 0.0510 | |
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| 0.0014 | 92.6108 | 9400 | 0.0001 | 0.0508 | |
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| 0.0016 | 94.5813 | 9600 | 0.0000 | 0.0510 | |
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| 0.0011 | 96.5517 | 9800 | 0.0000 | 0.0510 | |
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| 0.001 | 98.5222 | 10000 | 0.0000 | 0.0510 | |
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### Framework versions |
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- Transformers 4.45.0.dev0 |
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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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