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--- |
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language: |
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- br |
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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-xls-r-300m |
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datasets: |
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- mozilla-foundation/common_voice_15_0 |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-xls-r-300m-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-xls-r-300m-breton |
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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 [mozilla-foundation/common_voice_15_0]() dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9939 |
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- Wer: 0.4680 |
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- Cer: 0.1711 |
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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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| 10.4347 | 2.56 | 250 | 3.7906 | 1.0 | 1.0 | |
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| 3.2144 | 5.13 | 500 | 3.0622 | 1.0 | 1.0 | |
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| 2.6193 | 7.69 | 750 | 1.6063 | 0.9122 | 0.4042 | |
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| 1.3658 | 10.26 | 1000 | 1.0917 | 0.7061 | 0.2474 | |
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| 0.9614 | 12.82 | 1250 | 0.9805 | 0.6384 | 0.2245 | |
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| 0.7915 | 15.38 | 1500 | 0.9340 | 0.5989 | 0.2124 | |
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| 0.6728 | 17.95 | 1750 | 0.9135 | 0.5735 | 0.2016 | |
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| 0.5889 | 20.51 | 2000 | 0.9381 | 0.5443 | 0.1944 | |
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| 0.5374 | 23.08 | 2250 | 0.9517 | 0.5372 | 0.1918 | |
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| 0.4972 | 25.64 | 2500 | 0.9171 | 0.5134 | 0.1828 | |
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| 0.4537 | 28.21 | 2750 | 0.9457 | 0.5043 | 0.1804 | |
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| 0.4217 | 30.77 | 3000 | 0.9479 | 0.4957 | 0.1789 | |
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| 0.4082 | 33.33 | 3250 | 0.9400 | 0.4906 | 0.1762 | |
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| 0.4005 | 35.9 | 3500 | 0.9710 | 0.4881 | 0.1770 | |
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| 0.3702 | 38.46 | 3750 | 0.9922 | 0.4792 | 0.1757 | |
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| 0.3473 | 41.03 | 4000 | 0.9724 | 0.4760 | 0.1734 | |
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| 0.3405 | 43.59 | 4250 | 0.9860 | 0.4726 | 0.1722 | |
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| 0.325 | 46.15 | 4500 | 0.9888 | 0.4679 | 0.1705 | |
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| 0.3193 | 48.72 | 4750 | 0.9939 | 0.4680 | 0.1711 | |
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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 2.16.0 |
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- Tokenizers 0.15.0 |
