capstone_model_small
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1606
- Accuracy: 0.9822
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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.3588 | 1.0 | 897 | 0.0761 | 0.9844 |
0.0651 | 2.0 | 1794 | 0.0560 | 0.9844 |
0.056 | 3.0 | 2691 | 0.0817 | 0.9866 |
0.0262 | 4.0 | 3588 | 0.1185 | 0.9866 |
0.0274 | 5.0 | 4485 | 0.0793 | 0.9889 |
0.007 | 6.0 | 5382 | 0.0958 | 0.9889 |
0.0085 | 7.0 | 6279 | 0.0765 | 0.9889 |
0.0 | 8.0 | 7176 | 0.1577 | 0.9822 |
0.0 | 9.0 | 8073 | 0.1604 | 0.9822 |
0.0 | 10.0 | 8970 | 0.1606 | 0.9822 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for dgambone/capstone_model_small
Base model
distilbert/distilbert-base-uncased