bert-base-uncased-finetuned-filtered-0609
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: 0.1749
- Accuracy: 0.9789
- Precision: 0.9790
- Recall: 0.9789
- F1: 0.9789
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.1671 | 1.0 | 3180 | 0.1735 | 0.9632 | 0.9648 | 0.9632 | 0.9635 |
| 0.1384 | 2.0 | 6360 | 0.1120 | 0.9736 | 0.9738 | 0.9736 | 0.9736 |
| 0.1064 | 3.0 | 9540 | 0.1880 | 0.9635 | 0.9647 | 0.9635 | 0.9635 |
| 0.0823 | 4.0 | 12720 | 0.1495 | 0.9758 | 0.9759 | 0.9758 | 0.9757 |
| 0.0426 | 5.0 | 15900 | 0.1766 | 0.9742 | 0.9746 | 0.9742 | 0.9743 |
| 0.0254 | 6.0 | 19080 | 0.1724 | 0.9777 | 0.9778 | 0.9777 | 0.9777 |
| 0.0257 | 7.0 | 22260 | 0.1760 | 0.9764 | 0.9767 | 0.9764 | 0.9764 |
| 0.0017 | 8.0 | 25440 | 0.1672 | 0.9786 | 0.9787 | 0.9786 | 0.9786 |
| 0.0077 | 9.0 | 28620 | 0.1894 | 0.9789 | 0.9791 | 0.9789 | 0.9789 |
| 0.0014 | 10.0 | 31800 | 0.1749 | 0.9789 | 0.9790 | 0.9789 | 0.9789 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.9.1+cu111
- Datasets 1.16.1
- Tokenizers 0.12.1