corr2cause-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: 0.2883
- Accuracy: 0.8795
- Macro F1: 0.7028
- Micro F1: 0.8795
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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Micro F1 |
|---|---|---|---|---|---|---|
| 0.3703 | 1.0 | 3215 | 0.3348 | 0.8571 | 0.6841 | 0.8571 |
| 0.3587 | 2.0 | 6430 | 0.2942 | 0.8735 | 0.6869 | 0.8735 |
| 0.342 | 3.0 | 9645 | 0.2883 | 0.8795 | 0.7028 | 0.8795 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.11.0+cu130
- Datasets 3.6.0
- Tokenizers 0.21.4