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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_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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# gopdataset_phonome_base_add_transformer |
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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: 0.3081 |
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- Cer: 0.1141 |
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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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| 6.7266 | 0.84 | 100 | 3.4268 | 0.9750 | |
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| 3.258 | 1.68 | 200 | 3.2266 | 0.7902 | |
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| 2.5421 | 2.52 | 300 | 1.1589 | 0.5124 | |
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| 1.0681 | 3.36 | 400 | 0.4367 | 0.1676 | |
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| 0.7192 | 4.2 | 500 | 0.4418 | 0.1658 | |
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| 0.5793 | 5.04 | 600 | 0.3079 | 0.1331 | |
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| 0.5329 | 5.88 | 700 | 0.3078 | 0.1287 | |
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| 0.4988 | 6.72 | 800 | 0.3051 | 0.1251 | |
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| 0.4455 | 7.56 | 900 | 0.2843 | 0.1206 | |
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| 0.4271 | 8.4 | 1000 | 0.2865 | 0.1234 | |
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| 0.4027 | 9.24 | 1100 | 0.2996 | 0.1214 | |
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| 0.3939 | 10.08 | 1200 | 0.2874 | 0.1199 | |
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| 0.3633 | 10.92 | 1300 | 0.2777 | 0.1237 | |
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| 0.3482 | 11.76 | 1400 | 0.2648 | 0.1171 | |
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| 0.3267 | 12.61 | 1500 | 0.2737 | 0.1174 | |
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| 0.3334 | 13.45 | 1600 | 0.2812 | 0.1176 | |
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| 0.3145 | 14.29 | 1700 | 0.2709 | 0.1163 | |
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| 0.2921 | 15.13 | 1800 | 0.2689 | 0.1153 | |
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| 0.2939 | 15.97 | 1900 | 0.2757 | 0.1153 | |
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| 0.2681 | 16.81 | 2000 | 0.2785 | 0.1161 | |
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| 0.2691 | 17.65 | 2100 | 0.2955 | 0.1196 | |
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| 0.2627 | 18.49 | 2200 | 0.2922 | 0.1174 | |
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| 0.2519 | 19.33 | 2300 | 0.2820 | 0.1148 | |
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| 0.2391 | 20.17 | 2400 | 0.3038 | 0.1190 | |
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| 0.2393 | 21.01 | 2500 | 0.2873 | 0.1162 | |
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| 0.2324 | 21.85 | 2600 | 0.2903 | 0.1148 | |
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| 0.2217 | 22.69 | 2700 | 0.3018 | 0.1167 | |
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| 0.2156 | 23.53 | 2800 | 0.3033 | 0.1153 | |
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| 0.2039 | 24.37 | 2900 | 0.2975 | 0.1147 | |
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| 0.2018 | 25.21 | 3000 | 0.3055 | 0.1159 | |
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| 0.1996 | 26.05 | 3100 | 0.3035 | 0.1151 | |
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| 0.2013 | 26.89 | 3200 | 0.3032 | 0.1153 | |
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| 0.2002 | 27.73 | 3300 | 0.3029 | 0.1146 | |
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| 0.196 | 28.57 | 3400 | 0.3118 | 0.1157 | |
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| 0.2047 | 29.41 | 3500 | 0.3081 | 0.1141 | |
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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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