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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: wav2vec2-arabic-gpu-colab-similar-to-german-bigger-warm-up
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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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# wav2vec2-arabic-gpu-colab-similar-to-german-bigger-warm-up
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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: 0.6370
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- Wer: 0.4146
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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: 2
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 6
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- total_train_batch_size: 12
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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: 5000
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- num_epochs: 40
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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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| 9.4958 | 2.83 | 400 | 3.4822 | 1.0 |
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| 3.2281 | 5.67 | 800 | 2.9404 | 1.0 |
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| 2.942 | 8.51 | 1200 | 2.8690 | 1.0 |
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| 2.6346 | 11.35 | 1600 | 1.5452 | 0.9994 |
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| 1.3472 | 14.18 | 2000 | 0.8261 | 0.6853 |
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| 0.8972 | 17.02 | 2400 | 0.6812 | 0.5737 |
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| 0.6924 | 19.85 | 2800 | 0.6552 | 0.5291 |
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| 0.5687 | 22.69 | 3200 | 0.6108 | 0.4909 |
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| 0.4734 | 25.53 | 3600 | 0.5877 | 0.4674 |
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| 0.4029 | 28.37 | 4000 | 0.6204 | 0.4662 |
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| 0.3483 | 31.2 | 4400 | 0.5932 | 0.4451 |
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| 0.307 | 34.04 | 4800 | 0.6445 | 0.4392 |
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| 0.2722 | 36.88 | 5200 | 0.6126 | 0.4292 |
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| 0.2247 | 39.71 | 5600 | 0.6370 | 0.4146 |
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### Framework versions
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- Transformers 4.11.3
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- Pytorch 1.10.0+cu113
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- Datasets 1.18.3
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- Tokenizers 0.10.3
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