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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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model-index: |
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- name: wav2vec-read_aloud |
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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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# wav2vec-read_aloud |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1115 |
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- Pcc Accuracy: 0.7918 |
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- Pcc Fluency: 0.7940 |
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- Pcc Total Score: 0.8472 |
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- Pcc Content: 0.8160 |
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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: 0.00055 |
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- train_batch_size: 2 |
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- eval_batch_size: 6 |
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- seed: 42 |
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- gradient_accumulation_steps: 12 |
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- total_train_batch_size: 24 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.25 |
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- num_epochs: 14 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Pcc Accuracy | Pcc Fluency | Pcc Total Score | Pcc Content | |
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|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------:|:---------------:|:-----------:| |
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| 0.1483 | 1.94 | 500 | 0.1659 | 0.7256 | 0.6982 | 0.7616 | 0.7480 | |
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| 0.1338 | 3.89 | 1000 | 0.1369 | 0.7706 | 0.7680 | 0.8154 | 0.7835 | |
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| 0.124 | 5.83 | 1500 | 0.1754 | 0.6686 | 0.6459 | 0.7110 | 0.6823 | |
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| 0.1147 | 7.77 | 2000 | 0.1149 | 0.7838 | 0.7848 | 0.8368 | 0.8048 | |
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| 0.1024 | 9.72 | 2500 | 0.1135 | 0.7802 | 0.7819 | 0.8340 | 0.8048 | |
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| 0.0945 | 11.66 | 3000 | 0.1168 | 0.7891 | 0.7876 | 0.8418 | 0.8095 | |
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| 0.0945 | 13.61 | 3500 | 0.1115 | 0.7918 | 0.7940 | 0.8472 | 0.8160 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
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