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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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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-burak-new-v10-small |
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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-burak-new-v10-small |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3345 |
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- Wer: 0.2030 |
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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: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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: 500 |
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- num_epochs: 271 |
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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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| 6.1239 | 9.43 | 500 | 3.1263 | 1.0 | |
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| 1.7776 | 18.87 | 1000 | 0.3793 | 0.4838 | |
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| 0.5275 | 28.3 | 1500 | 0.2654 | 0.3379 | |
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| 0.3605 | 37.74 | 2000 | 0.2704 | 0.2953 | |
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| 0.2802 | 47.17 | 2500 | 0.2610 | 0.2911 | |
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| 0.2348 | 56.6 | 3000 | 0.2717 | 0.2677 | |
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| 0.2101 | 66.04 | 3500 | 0.2736 | 0.2691 | |
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| 0.1805 | 75.47 | 4000 | 0.2782 | 0.2595 | |
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| 0.1644 | 84.91 | 4500 | 0.2873 | 0.2491 | |
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| 0.1469 | 94.34 | 5000 | 0.3040 | 0.2381 | |
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| 0.138 | 103.77 | 5500 | 0.3205 | 0.2429 | |
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| 0.1247 | 113.21 | 6000 | 0.3217 | 0.2264 | |
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| 0.118 | 122.64 | 6500 | 0.3148 | 0.2244 | |
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| 0.1116 | 132.08 | 7000 | 0.3114 | 0.2209 | |
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| 0.1045 | 141.51 | 7500 | 0.3151 | 0.2175 | |
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| 0.0988 | 150.94 | 8000 | 0.3096 | 0.2092 | |
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| 0.0925 | 160.38 | 8500 | 0.3357 | 0.2230 | |
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| 0.0898 | 169.81 | 9000 | 0.3220 | 0.2099 | |
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| 0.0848 | 179.25 | 9500 | 0.3372 | 0.2209 | |
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| 0.0831 | 188.68 | 10000 | 0.3030 | 0.2030 | |
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| 0.0796 | 198.11 | 10500 | 0.3297 | 0.2127 | |
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| 0.0747 | 207.55 | 11000 | 0.3312 | 0.2134 | |
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| 0.0777 | 216.98 | 11500 | 0.3231 | 0.2168 | |
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| 0.0724 | 226.42 | 12000 | 0.3248 | 0.2078 | |
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| 0.0705 | 235.85 | 12500 | 0.3277 | 0.2023 | |
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| 0.0691 | 245.28 | 13000 | 0.3262 | 0.1996 | |
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| 0.0661 | 254.72 | 13500 | 0.3356 | 0.1996 | |
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| 0.0678 | 264.15 | 14000 | 0.3345 | 0.2030 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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