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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: gopdatastt_add_transformer |
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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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# gopdatastt_add_transformer |
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This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0920 |
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- Wer: 0.1617 |
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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: 1000 |
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- num_epochs: 30 |
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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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| 3.1709 | 1.05 | 500 | 0.1453 | 0.2194 | |
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| 0.3131 | 2.11 | 1000 | 0.1094 | 0.2055 | |
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| 0.276 | 3.16 | 1500 | 0.1198 | 0.1998 | |
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| 0.2416 | 4.21 | 2000 | 0.1873 | 0.2026 | |
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| 0.2093 | 5.26 | 2500 | 0.1392 | 0.1974 | |
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| 0.1987 | 6.32 | 3000 | 0.1123 | 0.1944 | |
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| 0.1714 | 7.37 | 3500 | 0.1089 | 0.1890 | |
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| 0.1634 | 8.42 | 4000 | 0.1007 | 0.1863 | |
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| 0.1459 | 9.47 | 4500 | 0.1340 | 0.1864 | |
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| 0.1461 | 10.53 | 5000 | 0.1016 | 0.1874 | |
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| 0.1316 | 11.58 | 5500 | 0.1110 | 0.1891 | |
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| 0.1318 | 12.63 | 6000 | 0.0942 | 0.1855 | |
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| 0.1084 | 13.68 | 6500 | 0.0992 | 0.1827 | |
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| 0.1064 | 14.74 | 7000 | 0.1010 | 0.1801 | |
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| 0.1059 | 15.79 | 7500 | 0.1173 | 0.1834 | |
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| 0.094 | 16.84 | 8000 | 0.1096 | 0.1815 | |
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| 0.0918 | 17.89 | 8500 | 0.1046 | 0.1780 | |
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| 0.0874 | 18.95 | 9000 | 0.1103 | 0.1788 | |
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| 0.0813 | 20.0 | 9500 | 0.1065 | 0.1768 | |
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| 0.0753 | 21.05 | 10000 | 0.0997 | 0.1747 | |
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| 0.0729 | 22.11 | 10500 | 0.1053 | 0.1748 | |
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| 0.0655 | 23.16 | 11000 | 0.1042 | 0.1726 | |
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| 0.0647 | 24.21 | 11500 | 0.0960 | 0.1746 | |
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| 0.0581 | 25.26 | 12000 | 0.1060 | 0.1733 | |
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| 0.0573 | 26.32 | 12500 | 0.0972 | 0.1706 | |
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| 0.0524 | 27.37 | 13000 | 0.0963 | 0.1725 | |
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| 0.0577 | 28.42 | 13500 | 0.0920 | 0.1696 | |
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| 0.0488 | 29.47 | 14000 | 0.0942 | 0.1686 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 1.18.3 |
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- Tokenizers 0.20.3 |
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