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bert-base-uncased-finetuned-detests-29-10-2022
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1346
- Accuracy: 0.7921
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2105 | 0.33 | 50 | 0.5718 | 0.8265 |
| 0.2156 | 0.65 | 100 | 0.5998 | 0.8232 |
| 0.215 | 0.98 | 150 | 0.5778 | 0.8232 |
| 0.1353 | 1.31 | 200 | 0.6240 | 0.8069 |
| 0.0664 | 1.63 | 250 | 0.7277 | 0.7938 |
| 0.2339 | 1.96 | 300 | 0.8471 | 0.7758 |
| 0.1518 | 2.29 | 350 | 0.9487 | 0.7938 |
| 0.0766 | 2.61 | 400 | 0.9715 | 0.8069 |
| 0.0524 | 2.94 | 450 | 1.0911 | 0.7610 |
| 0.0836 | 3.27 | 500 | 1.0099 | 0.8101 |
| 0.0935 | 3.59 | 550 | 0.9368 | 0.8020 |
| 0.1065 | 3.92 | 600 | 0.9528 | 0.8282 |
| 0.0139 | 4.25 | 650 | 1.0382 | 0.7971 |
| 0.0642 | 4.58 | 700 | 1.1667 | 0.7774 |
| 0.1584 | 4.9 | 750 | 1.1346 | 0.7921 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
