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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: xls-r_cv_ur |
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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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# xls-r_cv_ur |
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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: 0.4888 |
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- Wer: 1.0 |
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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: 100 |
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- num_epochs: 20 |
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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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| 8.4361 | 1.14 | 100 | 2.3733 | 1.0 | |
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| 1.9609 | 2.27 | 200 | 1.8280 | 1.0 | |
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| 1.8191 | 3.41 | 300 | 1.8259 | 1.0 | |
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| 1.8359 | 4.55 | 400 | 1.8069 | 1.0 | |
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| 1.7667 | 5.68 | 500 | 1.8038 | 1.0 | |
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| 1.7751 | 6.82 | 600 | 1.7473 | 1.0 | |
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| 1.7428 | 7.95 | 700 | 1.6996 | 1.0 | |
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| 1.697 | 9.09 | 800 | 1.6364 | 1.0 | |
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| 1.6532 | 10.23 | 900 | 1.4985 | 1.0 | |
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| 1.5217 | 11.36 | 1000 | 1.2836 | 1.0 | |
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| 1.3385 | 12.5 | 1100 | 1.0293 | 1.0 | |
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| 1.1596 | 13.64 | 1200 | 0.8294 | 1.0 | |
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| 1.0655 | 14.77 | 1300 | 0.7150 | 1.0 | |
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| 0.9951 | 15.91 | 1400 | 0.6364 | 1.0 | |
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| 0.9013 | 17.05 | 1500 | 0.5548 | 1.0 | |
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| 0.8276 | 18.18 | 1600 | 0.5200 | 1.0 | |
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| 0.8129 | 19.32 | 1700 | 0.4888 | 1.0 | |
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### Framework versions |
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- Transformers 4.21.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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