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---
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license: apache-2.0
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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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model-index:
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- name: bsc_ai_thesis_torgo_model-1
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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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# bsc_ai_thesis_torgo_model-1
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3445
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- Accuracy: 0.8675
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- Precision: 0.8670
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- Recall: 0.8873
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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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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
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| 0.6837 | 0.96 | 12 | 0.6315 | 0.695 | 0.6717 | 0.8357 |
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| 0.576 | 2.0 | 25 | 0.4980 | 0.7675 | 0.8125 | 0.7324 |
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| 0.5089 | 2.96 | 37 | 0.4431 | 0.8125 | 0.8255 | 0.8216 |
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| 0.405 | 4.0 | 50 | 0.4718 | 0.8325 | 0.8687 | 0.8075 |
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| 0.3436 | 4.96 | 62 | 0.4669 | 0.795 | 0.7331 | 0.9671 |
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| 0.318 | 6.0 | 75 | 0.5111 | 0.7225 | 0.6903 | 0.8685 |
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| 0.2837 | 6.96 | 87 | 0.5359 | 0.7625 | 0.7783 | 0.7746 |
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| 0.2361 | 8.0 | 100 | 0.4633 | 0.805 | 0.8358 | 0.7887 |
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| 0.2327 | 8.96 | 112 | 0.3232 | 0.88 | 0.8733 | 0.9061 |
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| 0.2036 | 9.6 | 120 | 0.3445 | 0.8675 | 0.8670 | 0.8873 |
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### Framework versions
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- Transformers 4.28.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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