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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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- asr |
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- w2v-bert-2.0 |
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datasets: |
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- common_voice_16_1 |
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metrics: |
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- wer |
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- cer |
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- bertscore |
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model-index: |
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- name: w2v-bert-2.0-pt_pt_v2 |
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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: common_voice_16_1 |
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type: common_voice_16_1 |
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config: pt |
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split: validation |
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args: pt |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.08315087821729188 |
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language: |
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- pt |
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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-pt_pt_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 common_voice_16_1 Portuguese subset using 1XRTX 3090. |
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It achieves the following results on the test set: |
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- Wer: 0.10491320595991134 |
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- Cer: 0.032070871626631914 |
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- Bert Score: 0.9619712047981167 |
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- Sentence Similarity: 0.93867844 |
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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 | Cer | Bert Score | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:----------:| |
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| 1.2735 | 1.0 | 678 | 0.2292 | 0.1589 | 0.0415 | 0.9498 | |
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| 0.1715 | 2.0 | 1356 | 0.1762 | 0.1283 | 0.0344 | 0.9599 | |
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| 0.1158 | 3.0 | 2034 | 0.1539 | 0.1100 | 0.0298 | 0.9646 | |
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| 0.0821 | 4.0 | 2712 | 0.1362 | 0.0949 | 0.0258 | 0.9703 | |
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| 0.0605 | 5.0 | 3390 | 0.1349 | 0.0860 | 0.0236 | 0.9728 | |
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| 0.0475 | 6.0 | 4068 | 0.1395 | 0.0871 | 0.0239 | 0.9728 | |
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| 0.0355 | 7.0 | 4746 | 0.1487 | 0.0837 | 0.0230 | 0.9739 | |
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| 0.0309 | 8.0 | 5424 | 0.1452 | 0.0873 | 0.0240 | 0.9728 | |
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| 0.0308 | 9.0 | 6102 | 0.1390 | 0.0843 | 0.0228 | 0.9735 | |
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| 0.0239 | 10.0 | 6780 | 0.1282 | 0.0832 | 0.0224 | 0.9739 | |
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### Evaluation results |
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| Test Wer | Test Cer | Test Bert Score | Runtime | Samples per second | |
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|:------------------:|:-------------------:|:-----------------:|:-------:|:---------------------:| |
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| 0.09146400542583083| 0.02643665913309742 | 0.9702128323433327| 266.8185| 35.282 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.0 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
