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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-cry-detector
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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: 0.8595505617977528
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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-cry-detector
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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: 0.7516
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- Accuracy: 0.8596
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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: 0.0001
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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: cosine
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- num_epochs: 6
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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 | 1.4029 | 0.5843 |
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| No log | 1.9775 | 22 | 1.0708 | 0.7022 |
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| No log | 2.9663 | 33 | 0.9015 | 0.8090 |
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| No log | 3.9551 | 44 | 0.8003 | 0.8427 |
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| No log | 4.9438 | 55 | 0.7582 | 0.8483 |
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| No log | 5.9326 | 66 | 0.7516 | 0.8596 |
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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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