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--- |
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library_name: transformers |
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base_model: S-Sethisak/xlsr-khmer-fleur-ex02 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: xlsr |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: fleurs |
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type: fleurs |
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config: km_kh |
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split: None |
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args: km_kh |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.6776300222422034 |
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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 |
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This model is a fine-tuned version of [S-Sethisak/xlsr-khmer-fleur-ex02](https://huggingface.co/S-Sethisak/xlsr-khmer-fleur-ex02) on the fleurs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8011 |
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- Wer: 0.6776 |
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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: 6.25e-06 |
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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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 800 |
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- training_steps: 8000 |
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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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| 2.1893 | 0.1434 | 400 | 1.3998 | 1.0 | |
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| 1.7694 | 0.2867 | 800 | 0.9742 | 0.9704 | |
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| 1.7196 | 0.4301 | 1200 | 0.8980 | 0.7788 | |
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| 1.8691 | 0.5735 | 1600 | 0.8685 | 0.7422 | |
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| 1.8432 | 0.7168 | 2000 | 0.8528 | 0.7295 | |
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| 1.8607 | 0.8602 | 2400 | 0.8395 | 0.7231 | |
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| 1.7744 | 1.0036 | 2800 | 0.8338 | 0.7122 | |
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| 1.6846 | 1.1470 | 3200 | 0.8259 | 0.7024 | |
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| 1.7989 | 1.2903 | 3600 | 0.8297 | 0.6974 | |
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| 1.5462 | 1.4337 | 4000 | 0.8212 | 0.6938 | |
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| 1.6145 | 1.5771 | 4400 | 0.8214 | 0.6908 | |
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| 1.4987 | 1.7204 | 4800 | 0.8172 | 0.6854 | |
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| 1.5861 | 1.8638 | 5200 | 0.8185 | 0.6835 | |
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| 1.6129 | 2.0072 | 5600 | 0.8144 | 0.6810 | |
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| 1.6523 | 2.1505 | 6000 | 0.8170 | 0.6788 | |
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| 1.5069 | 2.2939 | 6400 | 0.8116 | 0.6793 | |
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| 1.5815 | 2.4373 | 6800 | 0.8113 | 0.6780 | |
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| 1.4807 | 2.5806 | 7200 | 0.8069 | 0.6768 | |
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| 1.6869 | 2.7240 | 7600 | 0.8024 | 0.6777 | |
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| 1.712 | 2.8674 | 8000 | 0.8011 | 0.6776 | |
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### Framework versions |
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- Transformers 4.52.4 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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