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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: MilladRN |
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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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# MilladRN |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4355 |
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- Wer: 0.4907 |
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- Cer: 0.2802 |
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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: 8 |
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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: 4000 |
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- num_epochs: 750 |
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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 | Cer | |
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|:-------------:|:------:|:-----:|:---------------:|:------:|:------:| |
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| 3.3347 | 33.9 | 2000 | 2.2561 | 0.9888 | 0.6087 | |
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| 1.3337 | 67.8 | 4000 | 1.8137 | 0.6877 | 0.3407 | |
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| 0.6504 | 101.69 | 6000 | 2.0718 | 0.6245 | 0.3229 | |
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| 0.404 | 135.59 | 8000 | 2.2246 | 0.6004 | 0.3221 | |
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| 0.2877 | 169.49 | 10000 | 2.2624 | 0.5836 | 0.3107 | |
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| 0.2149 | 203.39 | 12000 | 2.3788 | 0.5279 | 0.2802 | |
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| 0.1693 | 237.29 | 14000 | 1.8928 | 0.5502 | 0.2937 | |
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| 0.1383 | 271.19 | 16000 | 2.7520 | 0.5725 | 0.3103 | |
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| 0.1169 | 305.08 | 18000 | 2.2552 | 0.5446 | 0.2968 | |
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| 0.1011 | 338.98 | 20000 | 2.6794 | 0.5725 | 0.3119 | |
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| 0.0996 | 372.88 | 22000 | 2.4704 | 0.5595 | 0.3142 | |
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| 0.0665 | 406.78 | 24000 | 2.9073 | 0.5836 | 0.3194 | |
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| 0.0538 | 440.68 | 26000 | 3.1357 | 0.5632 | 0.3213 | |
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| 0.0538 | 474.58 | 28000 | 2.5639 | 0.5613 | 0.3091 | |
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| 0.0493 | 508.47 | 30000 | 3.3801 | 0.5613 | 0.3119 | |
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| 0.0451 | 542.37 | 32000 | 3.5469 | 0.5428 | 0.3158 | |
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| 0.0307 | 576.27 | 34000 | 4.2243 | 0.5390 | 0.3126 | |
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| 0.0301 | 610.17 | 36000 | 3.6666 | 0.5297 | 0.2929 | |
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| 0.0269 | 644.07 | 38000 | 3.2164 | 0.5 | 0.2838 | |
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| 0.0182 | 677.97 | 40000 | 3.0557 | 0.4963 | 0.2779 | |
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| 0.0191 | 711.86 | 42000 | 3.5190 | 0.5130 | 0.2921 | |
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| 0.0133 | 745.76 | 44000 | 3.4355 | 0.4907 | 0.2802 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 1.18.3 |
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- Tokenizers 0.12.1 |
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