bert-base-uncased-finetuned-stationary-epoch-update
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: 0.4848
- Accuracy: 0.81
- F1: 0.8070
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: 64
- eval_batch_size: 64
- 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 |
|---|---|---|---|---|---|
| 0.5929 | 1.0 | 38 | 0.4930 | 0.7567 | 0.7336 |
| 0.4451 | 2.0 | 76 | 0.4422 | 0.8067 | 0.8076 |
| 0.3475 | 3.0 | 114 | 0.4470 | 0.81 | 0.8115 |
| 0.2764 | 4.0 | 152 | 0.5021 | 0.78 | 0.7693 |
| 0.2205 | 5.0 | 190 | 0.4848 | 0.81 | 0.8070 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0