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bert-cased-exist-2
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4661
- Accuracy: 0.7762
- F1: 0.7762
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 | F1 |
---|---|---|---|---|---|
No log | 1.0 | 194 | 0.5088 | 0.7558 | 0.7558 |
No log | 2.0 | 388 | 0.5242 | 0.7587 | 0.7587 |
0.4312 | 3.0 | 582 | 0.6426 | 0.7471 | 0.7471 |
0.4312 | 4.0 | 776 | 0.9929 | 0.7529 | 0.7529 |
0.4312 | 5.0 | 970 | 1.1684 | 0.7645 | 0.7645 |
0.104 | 6.0 | 1164 | 1.2257 | 0.7849 | 0.7849 |
0.104 | 7.0 | 1358 | 1.4336 | 0.7645 | 0.7645 |
0.0161 | 8.0 | 1552 | 1.3932 | 0.7762 | 0.7762 |
0.0161 | 9.0 | 1746 | 1.4479 | 0.7820 | 0.7820 |
0.0161 | 10.0 | 1940 | 1.4661 | 0.7762 | 0.7762 |
Framework versions
- Transformers 4.27.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2