mnist-digit-recognition
This model is a fine-tuned version of scratch on the mnist dataset. It achieves the following results on the evaluation set:
- Loss: 0.0181
- Accuracy: 0.9943
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: 0.0003
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0684 | 1.0 | 422 | 0.0517 | 0.9853 |
| 0.0451 | 2.0 | 844 | 0.0343 | 0.9888 |
| 0.039 | 3.0 | 1266 | 0.0289 | 0.9907 |
| 0.0296 | 4.0 | 1688 | 0.0280 | 0.9917 |
| 0.0242 | 5.0 | 2110 | 0.0264 | 0.9918 |
Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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