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bert-ner-2
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: 0.7358
- Precision: 0.1646
- Recall: 0.4605
- F1: 0.2425
- Accuracy: 0.8784
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: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 149 | 0.5651 | 0.1347 | 0.4192 | 0.2038 | 0.8686 |
No log | 2.0 | 298 | 0.5818 | 0.1440 | 0.4227 | 0.2148 | 0.8785 |
No log | 3.0 | 447 | 0.6011 | 0.1432 | 0.3986 | 0.2107 | 0.8808 |
0.0328 | 4.0 | 596 | 0.5546 | 0.1613 | 0.3986 | 0.2297 | 0.8955 |
0.0328 | 5.0 | 745 | 0.7685 | 0.1371 | 0.4467 | 0.2098 | 0.8600 |
0.0328 | 6.0 | 894 | 0.7755 | 0.1486 | 0.4570 | 0.2243 | 0.8686 |
0.0102 | 7.0 | 1043 | 0.6831 | 0.1669 | 0.4570 | 0.2445 | 0.8834 |
0.0102 | 8.0 | 1192 | 0.7698 | 0.1524 | 0.4639 | 0.2294 | 0.8715 |
0.0102 | 9.0 | 1341 | 0.7303 | 0.1681 | 0.4708 | 0.2477 | 0.8791 |
0.0102 | 10.0 | 1490 | 0.7358 | 0.1646 | 0.4605 | 0.2425 | 0.8784 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1