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--- |
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library_name: transformers |
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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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metrics: |
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- wer |
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model-index: |
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- name: ssc-led-mms-model |
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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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# ssc-led-mms-model |
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This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4620 |
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- Cer: 0.1094 |
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- Wer: 0.3057 |
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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: 8 |
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- eval_batch_size: 12 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 10 |
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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 | Wer | |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------:| |
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| 2.1523 | 0.2080 | 200 | 1.1494 | 0.3505 | 0.9158 | |
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| 1.5516 | 0.4160 | 400 | 0.7710 | 0.1949 | 0.5516 | |
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| 1.4143 | 0.6240 | 600 | 0.7063 | 0.1745 | 0.5122 | |
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| 1.3084 | 0.8320 | 800 | 0.6080 | 0.1513 | 0.4280 | |
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| 1.1718 | 1.0395 | 1000 | 0.6245 | 0.1469 | 0.4409 | |
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| 1.1952 | 1.2475 | 1200 | 0.5926 | 0.1415 | 0.4116 | |
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| 1.1589 | 1.4555 | 1400 | 0.5521 | 0.1373 | 0.3912 | |
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| 1.1722 | 1.6635 | 1600 | 0.5385 | 0.1328 | 0.3748 | |
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| 1.1743 | 1.8716 | 1800 | 0.5272 | 0.1305 | 0.3714 | |
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| 1.1037 | 2.0790 | 2000 | 0.5284 | 0.1296 | 0.3676 | |
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| 1.1259 | 2.2871 | 2200 | 0.5284 | 0.1281 | 0.3654 | |
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| 1.1265 | 2.4951 | 2400 | 0.5120 | 0.1264 | 0.3555 | |
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| 1.0877 | 2.7031 | 2600 | 0.5108 | 0.1229 | 0.3493 | |
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| 1.0929 | 2.9111 | 2800 | 0.5011 | 0.1232 | 0.3480 | |
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| 1.0686 | 3.1186 | 3000 | 0.4911 | 0.1208 | 0.3389 | |
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| 1.0356 | 3.3266 | 3200 | 0.4911 | 0.1192 | 0.3331 | |
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| 0.9567 | 3.5346 | 3400 | 0.4872 | 0.1190 | 0.3376 | |
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| 1.1178 | 3.7426 | 3600 | 0.4824 | 0.1189 | 0.3322 | |
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| 1.0506 | 3.9506 | 3800 | 0.4804 | 0.1183 | 0.3391 | |
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| 1.0343 | 4.1581 | 4000 | 0.4853 | 0.1202 | 0.3422 | |
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| 0.9977 | 4.3661 | 4200 | 0.4819 | 0.1174 | 0.3226 | |
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| 1.0162 | 4.5741 | 4400 | 0.4835 | 0.1148 | 0.3170 | |
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| 1.1006 | 4.7821 | 4600 | 0.4745 | 0.1172 | 0.3296 | |
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| 0.9562 | 4.9901 | 4800 | 0.4720 | 0.1170 | 0.3233 | |
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| 0.9926 | 5.1976 | 5000 | 0.4808 | 0.1141 | 0.3150 | |
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| 0.9625 | 5.4056 | 5200 | 0.4870 | 0.1143 | 0.3178 | |
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| 1.0531 | 5.6136 | 5400 | 0.4759 | 0.1140 | 0.3177 | |
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| 1.0173 | 5.8216 | 5600 | 0.4701 | 0.1159 | 0.3232 | |
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| 0.9915 | 6.0291 | 5800 | 0.4674 | 0.1129 | 0.3169 | |
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| 0.9667 | 6.2371 | 6000 | 0.4724 | 0.1117 | 0.3093 | |
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| 0.9627 | 6.4451 | 6200 | 0.4630 | 0.1123 | 0.3108 | |
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| 0.9505 | 6.6531 | 6400 | 0.4755 | 0.1119 | 0.3093 | |
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| 1.0117 | 6.8612 | 6600 | 0.4642 | 0.1119 | 0.3075 | |
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| 1.0087 | 7.0686 | 6800 | 0.4668 | 0.1115 | 0.3068 | |
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| 0.9271 | 7.2767 | 7000 | 0.4766 | 0.1116 | 0.3118 | |
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| 0.949 | 7.4847 | 7200 | 0.4631 | 0.1130 | 0.3150 | |
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| 0.8933 | 7.6927 | 7400 | 0.4639 | 0.1113 | 0.3107 | |
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| 0.9838 | 7.9007 | 7600 | 0.4662 | 0.1117 | 0.3080 | |
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| 0.9619 | 8.1082 | 7800 | 0.4757 | 0.1105 | 0.3063 | |
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| 0.9731 | 8.3162 | 8000 | 0.4655 | 0.1098 | 0.3040 | |
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| 0.9118 | 8.5242 | 8200 | 0.4722 | 0.1103 | 0.3070 | |
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| 0.9808 | 8.7322 | 8400 | 0.4699 | 0.1105 | 0.3082 | |
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| 0.8915 | 8.9402 | 8600 | 0.4681 | 0.1099 | 0.3059 | |
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| 0.923 | 9.1477 | 8800 | 0.4702 | 0.1100 | 0.3043 | |
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| 0.9328 | 9.3557 | 9000 | 0.4674 | 0.1099 | 0.3052 | |
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| 0.932 | 9.5637 | 9200 | 0.4656 | 0.1099 | 0.3056 | |
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| 0.9293 | 9.7717 | 9400 | 0.4623 | 0.1093 | 0.3058 | |
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| 0.9046 | 9.9797 | 9600 | 0.4620 | 0.1094 | 0.3057 | |
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### Framework versions |
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- Transformers 4.57.2 |
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- Pytorch 2.9.1+cu128 |
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- Datasets 3.6.0 |
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- Tokenizers 0.22.0 |
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