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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_6_datasets |
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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_6_datasets |
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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: 0.3804 |
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- Wer: 0.2629 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 500 |
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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 | Wer | |
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|:-------------:|:------:|:-----:|:---------------:|:------:| |
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| 1.1149 | 0.3795 | 600 | 0.5531 | 0.4947 | |
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| 0.2052 | 0.7590 | 1200 | 0.4347 | 0.4689 | |
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| 0.1576 | 1.1385 | 1800 | 0.3204 | 0.3717 | |
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| 0.1263 | 1.5180 | 2400 | 0.3928 | 0.4128 | |
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| 0.1205 | 1.8975 | 3000 | 0.3214 | 0.3607 | |
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| 0.0993 | 2.2770 | 3600 | 0.3063 | 0.3514 | |
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| 0.091 | 2.6565 | 4200 | 0.3078 | 0.3390 | |
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| 0.0877 | 3.0361 | 4800 | 0.2673 | 0.3165 | |
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| 0.0716 | 3.4156 | 5400 | 0.2798 | 0.3039 | |
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| 0.0681 | 3.7951 | 6000 | 0.2710 | 0.2948 | |
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| 0.0592 | 4.1746 | 6600 | 0.2728 | 0.3072 | |
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| 0.0525 | 4.5541 | 7200 | 0.2828 | 0.3133 | |
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| 0.0497 | 4.9336 | 7800 | 0.3039 | 0.3132 | |
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| 0.0402 | 5.3131 | 8400 | 0.2741 | 0.2832 | |
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| 0.0389 | 5.6926 | 9000 | 0.2837 | 0.3018 | |
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| 0.0371 | 6.0721 | 9600 | 0.2732 | 0.2830 | |
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| 0.0286 | 6.4516 | 10200 | 0.2998 | 0.2794 | |
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| 0.028 | 6.8311 | 10800 | 0.2904 | 0.2769 | |
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| 0.0232 | 7.2106 | 11400 | 0.3183 | 0.2752 | |
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| 0.0201 | 7.5901 | 12000 | 0.3045 | 0.2665 | |
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| 0.0197 | 7.9696 | 12600 | 0.3137 | 0.2733 | |
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| 0.0139 | 8.3491 | 13200 | 0.3438 | 0.2670 | |
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| 0.0128 | 8.7287 | 13800 | 0.3385 | 0.2651 | |
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| 0.0115 | 9.1082 | 14400 | 0.3669 | 0.2671 | |
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| 0.0079 | 9.4877 | 15000 | 0.3695 | 0.2613 | |
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| 0.008 | 9.8672 | 15600 | 0.3804 | 0.2629 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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