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ner_kaggle_class_prediction_model
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: 0.0191
- Precision: 0.9850
- Recall: 0.9830
- F1: 0.9840
- Accuracy: 0.9950
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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.1304 | 1.0 | 806 | 0.0202 | 0.9823 | 0.9794 | 0.9808 | 0.9940 |
0.0142 | 2.0 | 1612 | 0.0178 | 0.9819 | 0.9826 | 0.9823 | 0.9945 |
0.0081 | 3.0 | 2418 | 0.0191 | 0.9850 | 0.9830 | 0.9840 | 0.9950 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2