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--- |
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license: cc-by-nc-4.0 |
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base_model: facebook/mms-1b-all |
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tags: |
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- generated_from_trainer |
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datasets: |
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- generator |
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model-index: |
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- name: stt |
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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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# stt |
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This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1619 |
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- Wer Lug: 0.161 |
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- Wer Eng: 0.096 |
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- Wer Mean: 0.129 |
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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: 0.0003 |
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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_steps: 100 |
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- training_steps: 5000 |
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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 Lug | Wer Eng | Wer Mean | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:--------:| |
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| 0.1876 | 0.1 | 500 | 0.1711 | 0.183 | 0.106 | 0.144 | |
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| 0.1971 | 0.2 | 1000 | 0.1702 | 0.172 | 0.106 | 0.139 | |
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| 0.1898 | 0.3 | 1500 | 0.1687 | 0.168 | 0.108 | 0.138 | |
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| 0.1903 | 0.4 | 2000 | 0.1686 | 0.165 | 0.103 | 0.134 | |
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| 0.1888 | 0.5 | 2500 | 0.1663 | 0.165 | 0.096 | 0.131 | |
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| 0.1908 | 1.1 | 3000 | 0.1637 | 0.16 | 0.095 | 0.127 | |
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| 0.1792 | 1.2 | 3500 | 0.1642 | 0.157 | 0.094 | 0.125 | |
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| 0.1963 | 1.3 | 4000 | 0.1625 | 0.158 | 0.095 | 0.127 | |
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| 0.184 | 1.4 | 4500 | 0.1623 | 0.158 | 0.094 | 0.126 | |
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| 0.1888 | 1.5 | 5000 | 0.1619 | 0.161 | 0.096 | 0.129 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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