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--- |
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library_name: transformers |
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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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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: distilhubert-finetuned-cry-detector |
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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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# 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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2255 |
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- Accuracy: 0.9883 |
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- F1: 0.9883 |
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- Precision: 0.9883 |
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- Recall: 0.9883 |
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- Confusion Matrix: [[960, 10], [6, 389]] |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 123 |
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- gradient_accumulation_steps: 2 |
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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: cosine_with_restarts |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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- label_smoothing_factor: 0.1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Confusion Matrix | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:----------------------:| |
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| 0.3124 | 2.3256 | 100 | 0.2739 | 0.9641 | 0.9640 | 0.9640 | 0.9641 | [[948, 22], [27, 368]] | |
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| 0.2337 | 4.6512 | 200 | 0.2385 | 0.9736 | 0.9737 | 0.9737 | 0.9736 | [[950, 20], [16, 379]] | |
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| 0.2064 | 6.9767 | 300 | 0.2295 | 0.9832 | 0.9832 | 0.9832 | 0.9832 | [[958, 12], [11, 384]] | |
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| 0.2023 | 9.3023 | 400 | 0.2277 | 0.9868 | 0.9869 | 0.9870 | 0.9868 | [[957, 13], [5, 390]] | |
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| 0.2003 | 11.6279 | 500 | 0.2254 | 0.9875 | 0.9876 | 0.9876 | 0.9875 | [[960, 10], [7, 388]] | |
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| 0.2002 | 13.9535 | 600 | 0.2259 | 0.9875 | 0.9876 | 0.9876 | 0.9875 | [[959, 11], [6, 389]] | |
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| 0.1994 | 16.2791 | 700 | 0.2255 | 0.9883 | 0.9883 | 0.9883 | 0.9883 | [[960, 10], [6, 389]] | |
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| 0.1997 | 18.6047 | 800 | 0.2254 | 0.9883 | 0.9883 | 0.9883 | 0.9883 | [[960, 10], [6, 389]] | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Tokenizers 0.19.1 |
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