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bert-base-uncased-finetuned-fin
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: 0.3931
- Accuracy: 0.8873
- F1: 0.8902
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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.6478 | 1.0 | 134 | 0.4118 | 0.8293 | 0.8309 |
| 0.3304 | 2.0 | 268 | 0.3315 | 0.8653 | 0.8694 |
| 0.2221 | 3.0 | 402 | 0.3229 | 0.8756 | 0.8781 |
| 0.1752 | 4.0 | 536 | 0.3192 | 0.8891 | 0.8921 |
| 0.1457 | 5.0 | 670 | 0.3700 | 0.8840 | 0.8880 |
| 0.1315 | 6.0 | 804 | 0.3774 | 0.8854 | 0.8882 |
| 0.1172 | 7.0 | 938 | 0.3883 | 0.8849 | 0.8877 |
| 0.112 | 8.0 | 1072 | 0.3931 | 0.8873 | 0.8902 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
