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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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base_model: facebook/wav2vec2-large-xlsr-53 |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xlsr-53-breton |
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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-large-xlsr-53-breton |
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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: 0.9840 |
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- Wer: 0.5852 |
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- Cer: 0.2130 |
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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: 6e-05 |
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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: 2 |
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- total_train_batch_size: 32 |
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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_ratio: 0.08 |
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- num_epochs: 50 |
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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 | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| 11.8947 | 2.56 | 250 | 3.4769 | 1.0 | 0.9862 | |
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| 3.1668 | 5.13 | 500 | 3.0459 | 1.0 | 0.9862 | |
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| 2.6491 | 7.69 | 750 | 1.6416 | 0.9319 | 0.4441 | |
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| 1.4107 | 10.26 | 1000 | 1.1000 | 0.7751 | 0.2852 | |
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| 0.9989 | 12.82 | 1250 | 0.9827 | 0.7092 | 0.2578 | |
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| 0.8238 | 15.38 | 1500 | 0.9543 | 0.6864 | 0.2476 | |
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| 0.7193 | 17.95 | 1750 | 0.9241 | 0.6547 | 0.2371 | |
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| 0.6377 | 20.51 | 2000 | 0.9296 | 0.6452 | 0.2352 | |
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| 0.5865 | 23.08 | 2250 | 0.9287 | 0.6320 | 0.2301 | |
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| 0.541 | 25.64 | 2500 | 0.9359 | 0.6205 | 0.2231 | |
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| 0.4988 | 28.21 | 2750 | 0.9850 | 0.6149 | 0.2244 | |
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| 0.4691 | 30.77 | 3000 | 0.9566 | 0.6065 | 0.2192 | |
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| 0.4568 | 33.33 | 3250 | 0.9653 | 0.6019 | 0.2175 | |
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| 0.4485 | 35.9 | 3500 | 0.9760 | 0.5949 | 0.2175 | |
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| 0.4219 | 38.46 | 3750 | 0.9824 | 0.5926 | 0.2177 | |
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| 0.397 | 41.03 | 4000 | 0.9669 | 0.5885 | 0.2138 | |
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| 0.3912 | 43.59 | 4250 | 0.9857 | 0.5908 | 0.2145 | |
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| 0.3764 | 46.15 | 4500 | 0.9937 | 0.5886 | 0.2145 | |
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| 0.3742 | 48.72 | 4750 | 0.9840 | 0.5852 | 0.2130 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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