bert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5190
- Accuracy: 0.875
- F1: 0.9113
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: 16
- 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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 230 | 0.4085 | 0.8309 | 0.8804 |
| No log | 2.0 | 460 | 0.4265 | 0.8382 | 0.8911 |
| 0.413 | 3.0 | 690 | 0.4550 | 0.8603 | 0.8946 |
| 0.413 | 4.0 | 920 | 0.5190 | 0.875 | 0.9113 |
| 0.1281 | 5.0 | 1150 | 0.5478 | 0.8725 | 0.9091 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0