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validate_bert_large
This model is a fine-tuned version of bert-large-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0445
- F1: 0.9828
- Roc Auc: 0.9871
- Accuracy: 0.9595
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
---|---|---|---|---|---|---|
0.1224 | 1.0 | 4000 | 0.1122 | 0.9414 | 0.9558 | 0.8664 |
0.1063 | 2.0 | 8000 | 0.0881 | 0.9583 | 0.9681 | 0.9010 |
0.0922 | 3.0 | 12000 | 0.0806 | 0.9623 | 0.9714 | 0.9085 |
0.0673 | 4.0 | 16000 | 0.0610 | 0.9740 | 0.9814 | 0.9370 |
0.0468 | 5.0 | 20000 | 0.0462 | 0.9812 | 0.9855 | 0.9545 |
0.0369 | 6.0 | 24000 | 0.0445 | 0.9828 | 0.9871 | 0.9595 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2