distilhubert-bass-classifier3
This model is a fine-tuned version of ntu-spml/distilhubert on the bass_design_encoded dataset. It achieves the following results on the evaluation set:
- Loss: 0.0115
- Accuracy: 0.9987
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.529 | 1.0 | 4301 | 0.8887 | 0.8651 |
0.0021 | 2.0 | 8602 | 0.3133 | 0.9477 |
0.0204 | 3.0 | 12903 | 0.0374 | 0.9919 |
0.2331 | 4.0 | 17204 | 0.1072 | 0.9833 |
0.0002 | 5.0 | 21505 | 0.0819 | 0.9890 |
0.0002 | 6.0 | 25806 | 0.0332 | 0.9948 |
0.1735 | 7.0 | 30107 | 0.0399 | 0.9942 |
0.0 | 8.0 | 34408 | 0.0208 | 0.9974 |
0.0 | 9.0 | 38709 | 0.0100 | 0.9984 |
0.0 | 10.0 | 43010 | 0.0115 | 0.9987 |
Framework versions
- Transformers 4.39.2
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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