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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-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 [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 484.9289 |
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- Pcc Accuracy: -0.1604 |
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- Pcc Fluency: -0.1393 |
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- Pcc Total Score: -0.1591 |
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- Pcc Content: -0.1544 |
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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: 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 | Pcc Accuracy | Pcc Fluency | Pcc Total Score | Pcc Content | |
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|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------:|:---------------:|:-----------:| |
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| 3175.5941 | 1.07 | 500 | 2802.1936 | -0.2863 | -0.2729 | -0.3001 | -0.2745 | |
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| 1733.457 | 2.13 | 1000 | 2440.8833 | -0.2827 | -0.2779 | -0.2959 | -0.2787 | |
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| 1890.6879 | 3.2 | 1500 | 1470.4958 | -0.2806 | -0.2763 | -0.2933 | -0.2772 | |
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| 470.8979 | 4.27 | 2000 | 565.3928 | -0.2658 | -0.2589 | -0.2764 | -0.2621 | |
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| 881.7893 | 5.34 | 2500 | 501.9731 | -0.2331 | -0.2204 | -0.2394 | -0.2285 | |
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| 379.352 | 6.4 | 3000 | 497.4395 | -0.2040 | -0.1871 | -0.2068 | -0.1982 | |
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| 378.5915 | 7.47 | 3500 | 491.6927 | -0.1783 | -0.1590 | -0.1789 | -0.1726 | |
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| 539.6395 | 8.54 | 4000 | 487.6133 | -0.1639 | -0.1434 | -0.1631 | -0.1582 | |
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| 319.019 | 9.61 | 4500 | 484.9289 | -0.1604 | -0.1393 | -0.1591 | -0.1544 | |
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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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