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finetuned-bert-mrpc
This model is a fine-tuned version of bert-base-cased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.4588
- Accuracy: 0.8382
- F1: 0.8878
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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.579 | 1.0 | 230 | 0.4858 | 0.7745 | 0.8521 |
| 0.4163 | 2.0 | 460 | 0.4477 | 0.8088 | 0.8721 |
| 0.2533 | 3.0 | 690 | 0.4588 | 0.8382 | 0.8878 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
