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--- |
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license: apache-2.0 |
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base_model: facebook/hubert-base-ls960 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- Emo-Codec/CREMA-D_synth |
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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: hubert-base-ls960-tone-classification |
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results: |
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- task: |
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name: Audio Classification |
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type: audio-classification |
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dataset: |
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name: CREMA-D |
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type: Emo-Codec/CREMA-D_synth |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8016085790884718 |
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- name: Precision |
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type: precision |
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value: 0.8014677098753149 |
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- name: Recall |
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type: recall |
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value: 0.8016085790884718 |
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- name: F1 |
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type: f1 |
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value: 0.7989608760238184 |
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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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# hubert-base-ls960-tone-classification |
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This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the CREMA-D dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7499 |
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- Accuracy: 0.8016 |
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- Precision: 0.8015 |
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- Recall: 0.8016 |
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- F1: 0.7990 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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: 8 |
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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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| 1.4326 | 1.0 | 442 | 1.2934 | 0.5147 | 0.5889 | 0.5147 | 0.4878 | |
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| 1.0447 | 2.0 | 884 | 0.8590 | 0.7051 | 0.7570 | 0.7051 | 0.7125 | |
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| 0.775 | 3.0 | 1326 | 0.7668 | 0.7426 | 0.7589 | 0.7426 | 0.7404 | |
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| 0.6593 | 4.0 | 1768 | 0.8127 | 0.7265 | 0.7564 | 0.7265 | 0.7245 | |
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| 0.5014 | 5.0 | 2210 | 0.8670 | 0.7507 | 0.7631 | 0.7507 | 0.7436 | |
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| 0.48 | 6.0 | 2652 | 0.7473 | 0.7694 | 0.7739 | 0.7694 | 0.7623 | |
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| 0.3505 | 7.0 | 3094 | 0.7647 | 0.8016 | 0.8039 | 0.8016 | 0.7991 | |
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| 0.3223 | 8.0 | 3536 | 0.7499 | 0.8016 | 0.8015 | 0.8016 | 0.7990 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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