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bert-cased-exist-5
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: 1.8080
- Accuracy: 0.6395
- F1: 0.6395
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 | 1.2159 | 0.5203 | 0.5203 |
No log | 2.0 | 388 | 1.0133 | 0.6512 | 0.6512 |
1.1255 | 3.0 | 582 | 1.0045 | 0.6599 | 0.6599 |
1.1255 | 4.0 | 776 | 1.1064 | 0.6512 | 0.6512 |
1.1255 | 5.0 | 970 | 1.2129 | 0.6541 | 0.6541 |
0.4684 | 6.0 | 1164 | 1.4733 | 0.6221 | 0.6221 |
0.4684 | 7.0 | 1358 | 1.5667 | 0.6337 | 0.6337 |
0.1279 | 8.0 | 1552 | 1.7241 | 0.6279 | 0.6279 |
0.1279 | 9.0 | 1746 | 1.7824 | 0.6395 | 0.6395 |
0.1279 | 10.0 | 1940 | 1.8080 | 0.6395 | 0.6395 |
Framework versions
- Transformers 4.27.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2