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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: 0.0636
- Precision: 0.9316
- Recall: 0.9483
- F1: 0.9399
- Accuracy: 0.9860
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 |
|---|---|---|---|---|---|---|---|
| 0.0828 | 1.0 | 1756 | 0.0655 | 0.9189 | 0.9359 | 0.9273 | 0.9825 |
| 0.0395 | 2.0 | 3512 | 0.0574 | 0.9226 | 0.9467 | 0.9345 | 0.9855 |
| 0.0187 | 3.0 | 5268 | 0.0636 | 0.9316 | 0.9483 | 0.9399 | 0.9860 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
