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bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0619
- Precision: 0.9349
- Recall: 0.9492
- F1: 0.9420
- Accuracy: 0.9855
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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.088 | 1.0 | 1756 | 0.0654 | 0.9144 | 0.9403 | 0.9271 | 0.9831 |
0.0395 | 2.0 | 3512 | 0.0605 | 0.9274 | 0.9482 | 0.9377 | 0.9851 |
0.0213 | 3.0 | 5268 | 0.0619 | 0.9349 | 0.9492 | 0.9420 | 0.9855 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1