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--- |
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library_name: transformers |
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language: |
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- lg |
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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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datasets: |
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- yogera |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-bert |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Yogera |
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type: yogera |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.14867316851893853 |
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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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# wav2vec2-bert |
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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 Yogera dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2216 |
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- Wer: 0.1487 |
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- Cer: 0.0334 |
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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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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 100 |
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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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| 0.6681 | 1.0 | 235 | 0.2226 | 0.2616 | 0.0533 | |
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| 0.1666 | 2.0 | 470 | 0.1639 | 0.2013 | 0.0410 | |
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| 0.1249 | 3.0 | 705 | 0.1608 | 0.1912 | 0.0416 | |
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| 0.101 | 4.0 | 940 | 0.1573 | 0.1835 | 0.0416 | |
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| 0.0833 | 5.0 | 1175 | 0.1567 | 0.1697 | 0.0378 | |
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| 0.0715 | 6.0 | 1410 | 0.1589 | 0.1564 | 0.0346 | |
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| 0.0624 | 7.0 | 1645 | 0.1634 | 0.1728 | 0.0408 | |
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| 0.0541 | 8.0 | 1880 | 0.1592 | 0.1559 | 0.0341 | |
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| 0.0464 | 9.0 | 2115 | 0.1788 | 0.1546 | 0.0336 | |
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| 0.0434 | 10.0 | 2350 | 0.1641 | 0.1575 | 0.0353 | |
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| 0.0385 | 11.0 | 2585 | 0.1854 | 0.1498 | 0.0333 | |
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| 0.0358 | 12.0 | 2820 | 0.1915 | 0.1504 | 0.0345 | |
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| 0.0308 | 13.0 | 3055 | 0.1747 | 0.1514 | 0.0328 | |
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| 0.0283 | 14.0 | 3290 | 0.1966 | 0.1449 | 0.0329 | |
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| 0.0274 | 15.0 | 3525 | 0.1882 | 0.1535 | 0.0342 | |
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| 0.0246 | 16.0 | 3760 | 0.2199 | 0.1588 | 0.0362 | |
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| 0.0212 | 17.0 | 3995 | 0.2108 | 0.1572 | 0.0355 | |
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| 0.0188 | 18.0 | 4230 | 0.2173 | 0.1453 | 0.0320 | |
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| 0.017 | 19.0 | 4465 | 0.2358 | 0.1444 | 0.0324 | |
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| 0.0177 | 20.0 | 4700 | 0.2280 | 0.1548 | 0.0339 | |
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| 0.0174 | 21.0 | 4935 | 0.2142 | 0.1484 | 0.0322 | |
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| 0.0138 | 22.0 | 5170 | 0.2315 | 0.1489 | 0.0338 | |
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| 0.0122 | 23.0 | 5405 | 0.2116 | 0.1483 | 0.0341 | |
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| 0.0125 | 24.0 | 5640 | 0.2216 | 0.1487 | 0.0334 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
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