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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: working |
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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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# working |
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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: 0.2536 |
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- Wer: 0.5805 |
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- Cer: 0.0690 |
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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: 3e-05 |
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- train_batch_size: 2 |
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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: 4 |
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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: 2000 |
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- training_steps: 26000 |
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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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| 2.0464 | 1.49 | 1000 | 1.5865 | 0.9968 | 0.5743 | |
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| 1.2779 | 2.98 | 2000 | 0.4195 | 0.7539 | 0.1008 | |
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| 1.0865 | 4.46 | 3000 | 0.3272 | 0.6791 | 0.0852 | |
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| 1.0852 | 5.95 | 4000 | 0.3039 | 0.6409 | 0.0789 | |
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| 1.0193 | 7.44 | 5000 | 0.3009 | 0.6442 | 0.0809 | |
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| 0.9566 | 8.93 | 6000 | 0.2804 | 0.6182 | 0.0762 | |
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| 0.9086 | 10.42 | 7000 | 0.2842 | 0.6336 | 0.0772 | |
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| 0.9161 | 11.9 | 8000 | 0.2757 | 0.6044 | 0.0735 | |
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| 0.8569 | 13.39 | 9000 | 0.2809 | 0.6052 | 0.0748 | |
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| 0.8517 | 14.88 | 10000 | 0.2813 | 0.6166 | 0.0759 | |
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| 0.8304 | 16.37 | 11000 | 0.2808 | 0.6044 | 0.0759 | |
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| 0.8325 | 17.86 | 12000 | 0.2656 | 0.6060 | 0.0735 | |
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| 0.7918 | 19.35 | 13000 | 0.2676 | 0.5930 | 0.0730 | |
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| 0.8061 | 20.83 | 14000 | 0.2740 | 0.5890 | 0.0731 | |
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| 0.7206 | 22.32 | 15000 | 0.2735 | 0.6141 | 0.0730 | |
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| 0.7372 | 23.81 | 16000 | 0.2663 | 0.5857 | 0.0711 | |
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| 0.7285 | 25.3 | 17000 | 0.2708 | 0.5841 | 0.0723 | |
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| 0.6778 | 26.79 | 18000 | 0.2726 | 0.5825 | 0.0724 | |
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| 0.7117 | 28.27 | 19000 | 0.2737 | 0.5898 | 0.0723 | |
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| 0.6951 | 29.76 | 20000 | 0.2733 | 0.5890 | 0.0721 | |
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| 0.6775 | 31.25 | 21000 | 0.2721 | 0.5865 | 0.0725 | |
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| 0.666 | 32.74 | 22000 | 0.2733 | 0.5857 | 0.0721 | |
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| 0.688 | 34.23 | 23000 | 0.2700 | 0.5890 | 0.0718 | |
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| 0.6813 | 35.71 | 24000 | 0.2724 | 0.5930 | 0.0717 | |
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| 0.6409 | 37.2 | 25000 | 0.2704 | 0.5922 | 0.0714 | |
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| 0.6427 | 38.69 | 26000 | 0.2704 | 0.5881 | 0.0713 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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