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---
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license: apache-2.0
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base_model: facebook/wav2vec2-base
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: wav2vec2-classifier
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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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# wav2vec2-classifier
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.0684
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- Accuracy: 0.4474
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- Precision: 0.3646
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- Recall: 0.4474
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- F1: 0.3683
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- Binary: 0.6094
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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: 3e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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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 | Accuracy | Precision | Recall | F1 | Binary |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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| No log | 0.96 | 50 | 4.2611 | 0.0647 | 0.0196 | 0.0647 | 0.0252 | 0.3121 |
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| 4.403 | 1.91 | 100 | 3.9746 | 0.0889 | 0.0305 | 0.0889 | 0.0343 | 0.3509 |
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| 4.1379 | 2.87 | 150 | 3.7561 | 0.1698 | 0.0733 | 0.1698 | 0.0880 | 0.4135 |
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| 3.8988 | 3.83 | 200 | 3.5600 | 0.2372 | 0.1619 | 0.2372 | 0.1527 | 0.4652 |
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| 3.6407 | 4.78 | 250 | 3.4072 | 0.3019 | 0.2248 | 0.3019 | 0.2174 | 0.5100 |
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| 3.5551 | 5.74 | 300 | 3.2951 | 0.3720 | 0.2851 | 0.3720 | 0.2858 | 0.5555 |
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| 3.4319 | 6.7 | 350 | 3.2052 | 0.4070 | 0.3239 | 0.4070 | 0.3214 | 0.5803 |
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| 3.3287 | 7.66 | 400 | 3.1429 | 0.4151 | 0.3738 | 0.4151 | 0.3421 | 0.5868 |
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| 3.1949 | 8.61 | 450 | 3.0862 | 0.4555 | 0.3707 | 0.4555 | 0.3759 | 0.6178 |
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| 3.2056 | 9.57 | 500 | 3.0684 | 0.4474 | 0.3646 | 0.4474 | 0.3683 | 0.6094 |
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### Framework versions
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- Transformers 4.38.2
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- Pytorch 2.3.0
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- Datasets 2.19.1
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- Tokenizers 0.15.1
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