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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: gopdataset_phonome_base |
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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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# gopdataset_phonome_base |
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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: 0.2722 |
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- Cer: 0.1144 |
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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: 32 |
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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 | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 10.4672 | 0.84 | 100 | 12.3501 | 0.9750 | |
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| 5.6053 | 1.68 | 200 | 4.4724 | 0.9750 | |
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| 3.6779 | 2.52 | 300 | 3.5514 | 0.9750 | |
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| 3.3108 | 3.36 | 400 | 3.4045 | 0.9750 | |
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| 3.2684 | 4.2 | 500 | 3.4435 | 0.7828 | |
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| 3.1223 | 5.04 | 600 | 3.0123 | 0.7864 | |
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| 2.663 | 5.88 | 700 | 2.1177 | 0.6216 | |
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| 1.8146 | 6.72 | 800 | 0.9518 | 0.2387 | |
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| 1.0305 | 7.56 | 900 | 0.5432 | 0.1662 | |
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| 0.7835 | 8.4 | 1000 | 0.4268 | 0.1500 | |
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| 0.6468 | 9.24 | 1100 | 0.3911 | 0.1422 | |
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| 0.564 | 10.08 | 1200 | 0.3544 | 0.1378 | |
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| 0.5089 | 10.92 | 1300 | 0.3322 | 0.1356 | |
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| 0.4667 | 11.76 | 1400 | 0.3058 | 0.1277 | |
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| 0.4304 | 12.61 | 1500 | 0.2984 | 0.1248 | |
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| 0.4248 | 13.45 | 1600 | 0.3040 | 0.1270 | |
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| 0.4041 | 14.29 | 1700 | 0.2886 | 0.1223 | |
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| 0.3641 | 15.13 | 1800 | 0.2860 | 0.1215 | |
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| 0.3611 | 15.97 | 1900 | 0.2868 | 0.1220 | |
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| 0.3336 | 16.81 | 2000 | 0.2906 | 0.1217 | |
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| 0.3329 | 17.65 | 2100 | 0.2908 | 0.1213 | |
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| 0.3264 | 18.49 | 2200 | 0.2933 | 0.1204 | |
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| 0.3059 | 19.33 | 2300 | 0.2818 | 0.1193 | |
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| 0.2966 | 20.17 | 2400 | 0.2924 | 0.1196 | |
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| 0.2948 | 21.01 | 2500 | 0.2851 | 0.1186 | |
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| 0.2833 | 21.85 | 2600 | 0.2818 | 0.1181 | |
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| 0.2724 | 22.69 | 2700 | 0.2884 | 0.1183 | |
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| 0.2693 | 23.53 | 2800 | 0.2905 | 0.1179 | |
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| 0.2593 | 24.37 | 2900 | 0.2894 | 0.1184 | |
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| 0.2515 | 25.21 | 3000 | 0.2931 | 0.1169 | |
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| 0.2487 | 26.05 | 3100 | 0.2915 | 0.1176 | |
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| 0.2518 | 26.89 | 3200 | 0.2900 | 0.1176 | |
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| 0.2467 | 27.73 | 3300 | 0.2934 | 0.1175 | |
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| 0.246 | 28.57 | 3400 | 0.2965 | 0.1182 | |
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| 0.2537 | 29.41 | 3500 | 0.2948 | 0.1183 | |
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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 1.18.3 |
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- Tokenizers 0.20.3 |
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