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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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metrics: |
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- accuracy |
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model-index: |
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- name: arabic-alphabet-speech-classification |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/kichsan92/huggingface/runs/ww9x1oum) |
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# arabic-alphabet-speech-classification |
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0156 |
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- Accuracy: 0.9980 |
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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: 8 |
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- eval_batch_size: 8 |
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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: 10 |
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- mixed_precision_training: Native AMP |
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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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| 1.0669 | 1.0 | 2220 | 0.9510 | 0.7601 | |
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| 0.2059 | 2.0 | 4440 | 0.0944 | 0.9718 | |
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| 0.0457 | 3.0 | 6660 | 0.0452 | 0.9863 | |
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| 0.0067 | 4.0 | 8880 | 0.0475 | 0.9903 | |
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| 0.0001 | 5.0 | 11100 | 0.0316 | 0.9923 | |
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| 0.0121 | 6.0 | 13320 | 0.0377 | 0.9926 | |
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| 0.0001 | 7.0 | 15540 | 0.0214 | 0.9950 | |
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| 0.0 | 8.0 | 17760 | 0.0226 | 0.9968 | |
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| 0.0 | 9.0 | 19980 | 0.0156 | 0.9980 | |
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| 0.0 | 10.0 | 22200 | 0.0117 | 0.9977 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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