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Bert-SBP
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.7436
- Accuracy: 0.7505
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: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|
| 0.1251 | 1.0 | 1139 | 1.3079 | 0.7370 |
| 0.172 | 2.0 | 2278 | 1.2414 | 0.7341 |
| 0.2179 | 3.0 | 3417 | 1.2107 | 0.7409 |
| 0.1429 | 4.0 | 4556 | 1.3118 | 0.7402 |
| 0.1037 | 5.0 | 5695 | 1.4270 | 0.7417 |
| 0.0699 | 6.0 | 6834 | 1.5152 | 0.7474 |
| 0.0513 | 7.0 | 7973 | 1.6423 | 0.7462 |
| 0.0371 | 8.0 | 9112 | 1.6923 | 0.7503 |
| 0.0222 | 9.0 | 10251 | 1.7365 | 0.7499 |
| 0.0188 | 10.0 | 11390 | 1.7436 | 0.7505 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
