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--- |
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library_name: transformers |
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license: mit |
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base_model: facebook/w2v-bert-2.0 |
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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: w2v-bert-2.0-Fleurs_AMMI_AFRIVOICE_LRSC-ln-20hrs-v2 |
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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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# w2v-bert-2.0-Fleurs_AMMI_AFRIVOICE_LRSC-ln-20hrs-v2 |
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This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co./facebook/w2v-bert-2.0) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7994 |
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- Wer: 0.2042 |
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- Cer: 0.0647 |
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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: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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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 adamw_torch 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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- num_epochs: 80 |
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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 | Cer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
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| 2.2265 | 1.0 | 356 | 0.4952 | 0.3304 | 0.1008 | |
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| 0.5303 | 2.0 | 712 | 0.3928 | 0.2891 | 0.0862 | |
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| 0.4278 | 3.0 | 1068 | 0.3568 | 0.2574 | 0.0769 | |
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| 0.3675 | 4.0 | 1424 | 0.3322 | 0.2385 | 0.0735 | |
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| 0.3271 | 5.0 | 1780 | 0.3118 | 0.2411 | 0.0776 | |
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| 0.2848 | 6.0 | 2136 | 0.3208 | 0.2092 | 0.0659 | |
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| 0.2556 | 7.0 | 2492 | 0.3289 | 0.2090 | 0.0658 | |
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| 0.2265 | 8.0 | 2848 | 0.3280 | 0.2324 | 0.0812 | |
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| 0.2043 | 9.0 | 3204 | 0.3279 | 0.2206 | 0.0700 | |
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| 0.1865 | 10.0 | 3560 | 0.3551 | 0.2132 | 0.0676 | |
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| 0.1658 | 11.0 | 3916 | 0.3360 | 0.2108 | 0.0697 | |
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| 0.1521 | 12.0 | 4272 | 0.3539 | 0.2347 | 0.0785 | |
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| 0.1408 | 13.0 | 4628 | 0.3642 | 0.2124 | 0.0708 | |
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| 0.128 | 14.0 | 4984 | 0.3877 | 0.2141 | 0.0689 | |
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| 0.1159 | 15.0 | 5340 | 0.4239 | 0.1999 | 0.0635 | |
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| 0.1073 | 16.0 | 5696 | 0.4337 | 0.2142 | 0.0700 | |
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| 0.0975 | 17.0 | 6052 | 0.4401 | 0.2022 | 0.0653 | |
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| 0.0899 | 18.0 | 6408 | 0.4767 | 0.2101 | 0.0662 | |
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| 0.0857 | 19.0 | 6764 | 0.4927 | 0.2041 | 0.0677 | |
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| 0.0783 | 20.0 | 7120 | 0.5006 | 0.2091 | 0.0677 | |
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| 0.0714 | 21.0 | 7476 | 0.5030 | 0.2140 | 0.0709 | |
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| 0.0667 | 22.0 | 7832 | 0.5110 | 0.2144 | 0.0691 | |
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| 0.0665 | 23.0 | 8188 | 0.5129 | 0.2136 | 0.0669 | |
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| 0.0586 | 24.0 | 8544 | 0.5906 | 0.1980 | 0.0632 | |
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| 0.0527 | 25.0 | 8900 | 0.6139 | 0.2019 | 0.0631 | |
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| 0.0471 | 26.0 | 9256 | 0.5897 | 0.2127 | 0.0677 | |
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| 0.0425 | 27.0 | 9612 | 0.6123 | 0.2037 | 0.0655 | |
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| 0.0374 | 28.0 | 9968 | 0.5954 | 0.2154 | 0.0697 | |
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| 0.035 | 29.0 | 10324 | 0.6249 | 0.2017 | 0.0634 | |
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| 0.0311 | 30.0 | 10680 | 0.6401 | 0.2056 | 0.0649 | |
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| 0.0274 | 31.0 | 11036 | 0.6688 | 0.2105 | 0.0668 | |
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| 0.0243 | 32.0 | 11392 | 0.6783 | 0.2122 | 0.0661 | |
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| 0.0223 | 33.0 | 11748 | 0.6974 | 0.2062 | 0.0652 | |
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| 0.0188 | 34.0 | 12104 | 0.7393 | 0.2069 | 0.0662 | |
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| 0.0177 | 35.0 | 12460 | 0.7598 | 0.2065 | 0.0645 | |
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| 0.0168 | 36.0 | 12816 | 0.7584 | 0.2058 | 0.0640 | |
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| 0.0137 | 37.0 | 13172 | 0.8009 | 0.2052 | 0.0645 | |
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| 0.0122 | 38.0 | 13528 | 0.7671 | 0.2045 | 0.0644 | |
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| 0.0105 | 39.0 | 13884 | 0.7994 | 0.2042 | 0.0647 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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