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bert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of bert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.5500
- Accuracy: 0.8676
- F1: 0.9046
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 230 | 0.3669 | 0.8309 | 0.8796 |
| No log | 2.0 | 460 | 0.3704 | 0.8652 | 0.9076 |
| 0.3951 | 3.0 | 690 | 0.4974 | 0.8627 | 0.9041 |
| 0.3951 | 4.0 | 920 | 0.5454 | 0.8652 | 0.9053 |
| 0.0994 | 5.0 | 1150 | 0.5500 | 0.8676 | 0.9046 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
