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bert-base-uncased-guilt-detectionv2
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7730
- Accuracy: 0.7876
- F1: 0.7876
- Precision: 0.7880
- Recall: 0.7876
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
0.4529 | 1.0 | 2042 | 0.4393 | 0.7995 | 0.7995 | 0.7995 | 0.7995 |
0.3885 | 2.0 | 4084 | 0.4630 | 0.7990 | 0.7989 | 0.7991 | 0.7990 |
0.2709 | 3.0 | 6126 | 0.5564 | 0.7964 | 0.7963 | 0.7974 | 0.7964 |
0.1738 | 4.0 | 8168 | 0.6039 | 0.7889 | 0.7887 | 0.7897 | 0.7889 |
0.1208 | 5.0 | 10210 | 0.7918 | 0.7837 | 0.7831 | 0.7867 | 0.7837 |
0.0881 | 6.0 | 12252 | 0.7730 | 0.7876 | 0.7876 | 0.7880 | 0.7876 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2