counterbench-bert-base-uncased
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5241
- Accuracy: 0.7879
- Macro F1: 0.7850
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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 62 | 0.6508 | 0.6333 | 0.6301 |
| No log | 2.0 | 124 | 0.5327 | 0.7273 | 0.6997 |
| No log | 3.0 | 186 | 0.5242 | 0.7667 | 0.7501 |
| No log | 4.0 | 248 | 0.5243 | 0.7848 | 0.7821 |
| No log | 5.0 | 310 | 0.5185 | 0.7788 | 0.7738 |
Framework versions
- Transformers 5.17.0
- Pytorch 2.14.0+cu130
- Datasets 3.6.0
- Tokenizers 0.23.2