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--- |
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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-br |
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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-br |
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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: 0.6660 |
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- Wer: 42.4942 |
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- Cer: 13.6525 |
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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: 6e-05 |
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- train_batch_size: 4 |
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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: 8 |
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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_ratio: 0.08 |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 8001 |
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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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| 1.0369 | 0.58 | 500 | 1.2289 | 85.3288 | 32.8021 | |
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| 0.7211 | 1.16 | 1000 | 0.9727 | 70.1973 | 24.6147 | |
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| 0.5669 | 1.75 | 1500 | 0.8496 | 64.6176 | 21.7978 | |
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| 0.4229 | 2.33 | 2000 | 0.7448 | 57.2663 | 19.3988 | |
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| 0.4352 | 2.91 | 2500 | 0.6749 | 52.9790 | 17.4075 | |
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| 0.3392 | 3.49 | 3000 | 0.6703 | 50.9678 | 16.8375 | |
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| 0.2508 | 4.07 | 3500 | 0.6143 | 49.6249 | 16.2547 | |
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| 0.2303 | 4.65 | 4000 | 0.7121 | 48.4648 | 15.8534 | |
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| 0.1776 | 5.24 | 4500 | 0.6667 | 47.0777 | 15.2910 | |
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| 0.1645 | 5.82 | 5000 | 0.6715 | 46.1825 | 14.8910 | |
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| 0.1304 | 6.4 | 5500 | 0.7212 | 44.2784 | 14.5139 | |
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| 0.1157 | 6.98 | 6000 | 0.6678 | 44.2721 | 14.3043 | |
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| 0.0924 | 7.56 | 6500 | 0.6935 | 43.1310 | 13.9171 | |
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| 0.0517 | 8.14 | 7000 | 0.6746 | 42.8851 | 13.7599 | |
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| 0.0667 | 8.73 | 7500 | 0.6327 | 42.9733 | 13.8136 | |
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| 0.0483 | 9.31 | 8000 | 0.6660 | 42.4942 | 13.6525 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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