finetuned-bert-base-uncased-on-HOPE
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: 1.3515
- Accuracy: 0.5345
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.7368 | 1.0 | 289 | 1.6685 | 0.4526 |
| 1.3051 | 2.0 | 578 | 1.4303 | 0.5176 |
| 1.0563 | 3.0 | 867 | 1.3849 | 0.5438 |
| 1.1101 | 4.0 | 1156 | 1.4233 | 0.5185 |
| 0.9118 | 5.0 | 1445 | 1.5438 | 0.5023 |
| 0.7889 | 6.0 | 1734 | 1.6832 | 0.5014 |
| 0.4892 | 7.0 | 2023 | 1.8469 | 0.4824 |
| 0.3739 | 8.0 | 2312 | 2.0680 | 0.4734 |
| 0.3813 | 9.0 | 2601 | 2.1392 | 0.4706 |
| 0.3459 | 10.0 | 2890 | 2.2772 | 0.4761 |
| 0.2323 | 11.0 | 3179 | 2.3445 | 0.4688 |
| 0.1977 | 12.0 | 3468 | 2.4754 | 0.4761 |
| 0.2351 | 13.0 | 3757 | 2.5912 | 0.4661 |
| 0.1991 | 14.0 | 4046 | 2.6713 | 0.4743 |
| 0.2239 | 15.0 | 4335 | 2.7262 | 0.4706 |
| 0.155 | 16.0 | 4624 | 2.7958 | 0.4697 |
| 0.1675 | 17.0 | 4913 | 2.8367 | 0.4724 |
| 0.1471 | 18.0 | 5202 | 2.8619 | 0.4715 |
| 0.1973 | 19.0 | 5491 | 2.8744 | 0.4770 |
| 0.1902 | 20.0 | 5780 | 2.8865 | 0.4752 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1