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BERT
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8223
- Accuracy: 0.82
- Precision: 0.84
- Recall: 0.9130
- F1: 0.8750
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
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
---|---|---|---|---|---|---|---|
0.6778 | 1.0 | 50 | 0.6148 | 0.69 | 0.7794 | 0.7681 | 0.7737 |
0.5331 | 2.0 | 100 | 0.5578 | 0.8 | 0.8267 | 0.8986 | 0.8611 |
0.3768 | 3.0 | 150 | 0.5052 | 0.73 | 0.8889 | 0.6957 | 0.7805 |
0.2802 | 4.0 | 200 | 0.4998 | 0.86 | 0.8667 | 0.9420 | 0.9028 |
0.1869 | 5.0 | 250 | 0.5187 | 0.81 | 0.8906 | 0.8261 | 0.8571 |
0.1293 | 6.0 | 300 | 0.6516 | 0.85 | 0.8649 | 0.9275 | 0.8951 |
0.1165 | 7.0 | 350 | 0.6541 | 0.82 | 0.8806 | 0.8551 | 0.8676 |
0.0937 | 8.0 | 400 | 0.6855 | 0.84 | 0.8841 | 0.8841 | 0.8841 |
0.0791 | 9.0 | 450 | 0.7652 | 0.81 | 0.8472 | 0.8841 | 0.8652 |
0.0599 | 10.0 | 500 | 0.8223 | 0.82 | 0.84 | 0.9130 | 0.8750 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1