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---
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license: apache-2.0
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base_model: ntu-spml/distilhubert
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tags:
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- generated_from_trainer
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datasets:
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- audiofolder
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metrics:
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- accuracy
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model-index:
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- name: distilhubert-finetuned-donateacry
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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: audiofolder
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type: audiofolder
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config: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8932584269662921
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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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# distilhubert-finetuned-donateacry
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5034
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- Accuracy: 0.8933
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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: 0.001
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 123
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 64
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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: 25
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|
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| No log | 0.9888 | 11 | 0.9525 | 0.7303 |
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| No log | 1.9775 | 22 | 1.2765 | 0.5393 |
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| No log | 2.9663 | 33 | 0.6634 | 0.7978 |
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| No log | 3.9551 | 44 | 0.6369 | 0.8202 |
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| No log | 4.9438 | 55 | 0.5328 | 0.8596 |
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| No log | 5.9326 | 66 | 0.5146 | 0.8652 |
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| No log | 6.9213 | 77 | 0.5200 | 0.8764 |
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| No log | 8.0 | 89 | 0.5213 | 0.8708 |
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| No log | 8.9888 | 100 | 0.6062 | 0.8596 |
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| No log | 9.9775 | 111 | 0.5938 | 0.8652 |
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| No log | 10.9663 | 122 | 0.5247 | 0.8652 |
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| No log | 11.9551 | 133 | 0.7004 | 0.8483 |
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| No log | 12.9438 | 144 | 0.5388 | 0.8876 |
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| No log | 13.9326 | 155 | 0.4856 | 0.8876 |
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| No log | 14.9213 | 166 | 0.5380 | 0.8764 |
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| No log | 16.0 | 178 | 0.5055 | 0.8876 |
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| No log | 16.9888 | 189 | 0.5217 | 0.8876 |
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| No log | 17.9775 | 200 | 0.5034 | 0.8933 |
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| No log | 18.9663 | 211 | 0.4745 | 0.8876 |
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| No log | 19.9551 | 222 | 0.4812 | 0.8876 |
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| No log | 20.9438 | 233 | 0.4709 | 0.8820 |
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| No log | 21.9326 | 244 | 0.4824 | 0.8876 |
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| No log | 22.9213 | 255 | 0.4819 | 0.8876 |
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| No log | 24.0 | 267 | 0.4877 | 0.8933 |
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| No log | 24.7191 | 275 | 0.4866 | 0.8933 |
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### Framework versions
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- Transformers 4.42.3
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- Pytorch 2.3.1+cu118
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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