results
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: 0.3835
- Precision: 0.6242
- Recall: 0.6563
- F1: 0.6399
- Accuracy: 0.9043
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: 5e-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: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 368 | 0.3060 | 0.5800 | 0.6174 | 0.5981 | 0.8963 |
0.2936 | 2.0 | 736 | 0.2901 | 0.6033 | 0.6240 | 0.6135 | 0.8992 |
0.2936 | 3.0 | 1104 | 0.3063 | 0.6304 | 0.6364 | 0.6334 | 0.9052 |
0.1156 | 4.0 | 1472 | 0.3404 | 0.6293 | 0.6563 | 0.6425 | 0.9033 |
0.1156 | 5.0 | 1840 | 0.3835 | 0.6242 | 0.6563 | 0.6399 | 0.9043 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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