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--- |
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license: apache-2.0 |
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base_model: vitouphy/wav2vec2-xls-r-300m-english |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: wav2vec-reptiles |
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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-reptiles |
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This model is a fine-tuned version of [vitouphy/wav2vec2-xls-r-300m-english](https://huggingface.co/vitouphy/wav2vec2-xls-r-300m-english) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2223.3787 |
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- Pcc Accuracy: 0.2187 |
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- Pcc Fluency: 0.0834 |
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- Pcc Total Score: 0.1532 |
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- Pcc Content: 0.2235 |
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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: 4 |
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- eval_batch_size: 6 |
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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: cosine |
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- lr_scheduler_warmup_ratio: 0.4 |
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- num_epochs: 25 |
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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 | Pcc Accuracy | Pcc Fluency | Pcc Total Score | Pcc Content | |
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|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------:|:---------------:|:-----------:| |
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| 2728.7846 | 5.0 | 100 | 3196.0576 | 0.3476 | -0.2327 | -0.3110 | 0.2340 | |
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| 2491.7434 | 10.0 | 200 | 2875.6025 | 0.2791 | -0.0388 | -0.0724 | 0.2475 | |
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| 1926.2301 | 15.0 | 300 | 2480.8772 | 0.2280 | 0.0499 | 0.1131 | 0.2334 | |
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| 2065.1381 | 20.0 | 400 | 2265.0391 | 0.2201 | 0.0799 | 0.1478 | 0.2238 | |
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| 1903.073 | 25.0 | 500 | 2223.3787 | 0.2187 | 0.0834 | 0.1532 | 0.2235 | |
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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 2.17.0 |
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- Tokenizers 0.15.1 |
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