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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-repeat |
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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-repeat |
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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: 205.9549 |
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- Pcc Accuracy: 0.8004 |
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- Pcc Fluency: 0.7759 |
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- Pcc Total Score: 0.8207 |
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- Pcc Content: 0.7220 |
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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: 50 |
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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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| 507.295 | 3.54 | 500 | 538.7184 | 0.2592 | 0.2368 | 0.2807 | 0.3206 | |
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| 267.4833 | 7.08 | 1000 | 374.0983 | 0.5787 | 0.5582 | 0.5900 | 0.5040 | |
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| 246.7156 | 10.62 | 1500 | 483.3237 | 0.6618 | 0.6387 | 0.6761 | 0.5837 | |
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| 269.7238 | 14.16 | 2000 | 446.4642 | 0.6964 | 0.6691 | 0.7131 | 0.6288 | |
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| 289.3261 | 17.7 | 2500 | 244.4726 | 0.7201 | 0.6928 | 0.7371 | 0.6482 | |
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| 249.89 | 21.24 | 3000 | 413.8036 | 0.7340 | 0.7052 | 0.7548 | 0.6796 | |
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| 235.8593 | 24.78 | 3500 | 251.3629 | 0.7472 | 0.7217 | 0.7676 | 0.6808 | |
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| 217.7143 | 28.32 | 4000 | 212.4162 | 0.7779 | 0.7547 | 0.7973 | 0.6948 | |
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| 123.7326 | 31.86 | 4500 | 362.4697 | 0.7782 | 0.7528 | 0.7987 | 0.7062 | |
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| 132.7905 | 35.4 | 5000 | 228.9714 | 0.7826 | 0.7603 | 0.8021 | 0.6987 | |
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| 111.7989 | 38.94 | 5500 | 189.2367 | 0.7985 | 0.7754 | 0.8188 | 0.7169 | |
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| 104.5979 | 42.48 | 6000 | 271.8181 | 0.7929 | 0.7692 | 0.8143 | 0.7192 | |
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| 115.256 | 46.02 | 6500 | 220.4324 | 0.8008 | 0.7753 | 0.8209 | 0.7230 | |
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| 86.3804 | 49.56 | 7000 | 205.9549 | 0.8004 | 0.7759 | 0.8207 | 0.7220 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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