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--- |
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license: apache-2.0 |
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base_model: jonatasgrosman/wav2vec2-large-xlsr-53-english |
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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: speech-emotion-recognition |
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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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# speech-emotion-recognition |
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This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5578 |
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- Accuracy: 0.8225 |
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- Precision: 0.8278 |
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- Recall: 0.8225 |
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- F1: 0.8212 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 5 |
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- total_train_batch_size: 10 |
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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: 3 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.3499 | 1.0 | 394 | 1.2619 | 0.7120 | 0.7251 | 0.7120 | 0.7116 | |
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| 0.6955 | 2.0 | 788 | 0.7781 | 0.7799 | 0.7919 | 0.7799 | 0.7793 | |
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| 0.8665 | 3.0 | 1182 | 0.5578 | 0.8225 | 0.8278 | 0.8225 | 0.8212 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.1 |
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