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--- |
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license: mit |
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base_model: arslanarjumand/wav2vec-reptiles |
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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 [arslanarjumand/wav2vec-reptiles](https://huggingface.co/arslanarjumand/wav2vec-reptiles) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 180.5618 |
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- Pcc Accuracy: 0.7344 |
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- Pcc Fluency: 0.7572 |
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- Pcc Total Score: 0.7949 |
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- Pcc Content: 0.7727 |
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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: 2.5e-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: 4 |
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- total_train_batch_size: 16 |
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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: 0.5 |
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- num_epochs: 15 |
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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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| 323.2938 | 2.13 | 500 | 333.4772 | 0.4645 | 0.5166 | 0.5181 | 0.4915 | |
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| 274.2192 | 4.27 | 1000 | 259.5493 | 0.5725 | 0.6371 | 0.6430 | 0.6182 | |
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| 287.9362 | 6.4 | 1500 | 291.9187 | 0.6475 | 0.6895 | 0.7121 | 0.6902 | |
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| 273.6328 | 8.54 | 2000 | 229.1164 | 0.6884 | 0.7243 | 0.7522 | 0.7285 | |
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| 211.4504 | 10.67 | 2500 | 223.4485 | 0.7087 | 0.7420 | 0.7727 | 0.7499 | |
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| 162.7622 | 12.81 | 3000 | 180.6950 | 0.7302 | 0.7557 | 0.7918 | 0.7695 | |
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| 194.6916 | 14.94 | 3500 | 180.5618 | 0.7344 | 0.7572 | 0.7949 | 0.7727 | |
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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 2.17.1 |
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- Tokenizers 0.15.1 |
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