Bert-Thesis-NonKFold
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4861
- F1: 0.7464
- Recall: 0.7464
- Accuracy: 0.7464
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
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Accuracy |
---|---|---|---|---|---|---|
1.0891 | 1.0 | 1446 | 0.9053 | 0.7222 | 0.7222 | 0.7222 |
0.7239 | 2.0 | 2892 | 0.8697 | 0.7397 | 0.7397 | 0.7397 |
0.4902 | 3.0 | 4338 | 0.8814 | 0.7491 | 0.7491 | 0.7491 |
0.3287 | 4.0 | 5784 | 0.9655 | 0.7512 | 0.7512 | 0.7512 |
0.2156 | 5.0 | 7230 | 1.0648 | 0.7450 | 0.7450 | 0.7450 |
0.1473 | 6.0 | 8676 | 1.1826 | 0.7446 | 0.7446 | 0.7446 |
0.1071 | 7.0 | 10122 | 1.2922 | 0.7465 | 0.7465 | 0.7465 |
0.0692 | 8.0 | 11568 | 1.4034 | 0.7483 | 0.7483 | 0.7483 |
0.0511 | 9.0 | 13014 | 1.4611 | 0.7478 | 0.7478 | 0.7478 |
0.0386 | 10.0 | 14460 | 1.4861 | 0.7464 | 0.7464 | 0.7464 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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