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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-aiish-no |
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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-aiish-no |
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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: 0.3093 |
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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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| 4.5121 | 2.1505 | 200 | 2.5138 | 1.0 | |
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| 1.2964 | 4.3011 | 400 | 0.1073 | 0.4756 | |
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| 0.1795 | 6.4516 | 600 | 0.0381 | 0.3814 | |
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| 0.096 | 8.6022 | 800 | 0.0067 | 0.3117 | |
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| 0.0584 | 10.7527 | 1000 | 0.0140 | 0.3227 | |
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| 0.0457 | 12.9032 | 1200 | 0.0039 | 0.3130 | |
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| 0.0412 | 15.0538 | 1400 | 0.0024 | 0.3081 | |
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| 0.0269 | 17.2043 | 1600 | 0.0142 | 0.3093 | |
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| 0.0276 | 19.3548 | 1800 | 0.0013 | 0.3068 | |
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| 0.0279 | 21.5054 | 2000 | 0.0044 | 0.3117 | |
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| 0.0243 | 23.6559 | 2200 | 0.0026 | 0.3105 | |
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| 0.0178 | 25.8065 | 2400 | 0.0006 | 0.3081 | |
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| 0.0193 | 27.9570 | 2600 | 0.0115 | 0.3215 | |
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| 0.0237 | 30.1075 | 2800 | 0.0008 | 0.3068 | |
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| 0.0146 | 32.2581 | 3000 | 0.0011 | 0.3105 | |
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| 0.0109 | 34.4086 | 3200 | 0.0002 | 0.3068 | |
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| 0.0106 | 36.5591 | 3400 | 0.0011 | 0.3081 | |
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| 0.0171 | 38.7097 | 3600 | 0.0012 | 0.3093 | |
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| 0.0099 | 40.8602 | 3800 | 0.0002 | 0.3130 | |
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| 0.0102 | 43.0108 | 4000 | 0.0014 | 0.3154 | |
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| 0.0129 | 45.1613 | 4200 | 0.0003 | 0.3105 | |
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| 0.0108 | 47.3118 | 4400 | 0.0001 | 0.3068 | |
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| 0.0085 | 49.4624 | 4600 | 0.0001 | 0.3093 | |
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| 0.0067 | 51.6129 | 4800 | 0.0001 | 0.3081 | |
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| 0.0079 | 53.7634 | 5000 | 0.0006 | 0.3068 | |
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| 0.0078 | 55.9140 | 5200 | 0.0001 | 0.3093 | |
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| 0.0091 | 58.0645 | 5400 | 0.0020 | 0.3081 | |
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| 0.0071 | 60.2151 | 5600 | 0.0017 | 0.3154 | |
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| 0.004 | 62.3656 | 5800 | 0.0001 | 0.3105 | |
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| 0.004 | 64.5161 | 6000 | 0.0001 | 0.3093 | |
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| 0.0064 | 66.6667 | 6200 | 0.0096 | 0.3166 | |
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| 0.0048 | 68.8172 | 6400 | 0.0000 | 0.3068 | |
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| 0.0037 | 70.9677 | 6600 | 0.0321 | 0.3081 | |
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| 0.0041 | 73.1183 | 6800 | 0.0000 | 0.3093 | |
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| 0.0059 | 75.2688 | 7000 | 0.0013 | 0.3093 | |
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| 0.0019 | 77.4194 | 7200 | 0.0011 | 0.3081 | |
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| 0.0022 | 79.5699 | 7400 | 0.0000 | 0.3068 | |
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| 0.0022 | 81.7204 | 7600 | 0.0000 | 0.3068 | |
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| 0.004 | 83.8710 | 7800 | 0.0000 | 0.3081 | |
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| 0.0025 | 86.0215 | 8000 | 0.0000 | 0.3081 | |
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| 0.0032 | 88.1720 | 8200 | 0.0000 | 0.3081 | |
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| 0.0019 | 90.3226 | 8400 | 0.0000 | 0.3093 | |
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| 0.001 | 92.4731 | 8600 | 0.0000 | 0.3081 | |
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| 0.001 | 94.6237 | 8800 | 0.0000 | 0.3093 | |
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| 0.0018 | 96.7742 | 9000 | 0.0000 | 0.3093 | |
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| 0.0019 | 98.9247 | 9200 | 0.0000 | 0.3093 | |
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### Framework versions |
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- Transformers 4.45.0.dev0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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