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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-babycry-v7 |
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results: [] |
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datasets: |
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- Nooon/Donate_a_cry |
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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-babycry-v7 |
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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: 0.5864 |
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- Accuracy: {'accuracy': 0.8695652173913043} |
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- F1: 0.8089 |
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- Precision: 0.7561 |
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- Recall: 0.8696 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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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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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:------:|:---------:|:------:| |
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| 0.7417 | 0.5435 | 25 | 0.5925 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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| 0.7226 | 1.0870 | 50 | 0.6167 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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| 0.5606 | 1.6304 | 75 | 0.6808 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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| 0.8858 | 2.1739 | 100 | 0.5850 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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| 0.6573 | 2.7174 | 125 | 0.5968 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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| 0.7942 | 3.2609 | 150 | 0.6142 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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| 0.7497 | 3.8043 | 175 | 0.5915 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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| 0.7408 | 4.3478 | 200 | 0.5899 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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| 0.6499 | 4.8913 | 225 | 0.5989 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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| 0.6725 | 5.4348 | 250 | 0.5865 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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| 0.6797 | 5.9783 | 275 | 0.5852 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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| 0.6553 | 6.5217 | 300 | 0.5861 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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| 0.6535 | 7.0652 | 325 | 0.5863 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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| 0.7297 | 7.6087 | 350 | 0.5865 | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561 | 0.8696 | |
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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 |
