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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: 0.0592
- Precision: 0.9388
- Recall: 0.9522
- F1: 0.9454
- Accuracy: 0.9870
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 |
---|---|---|---|---|---|---|---|
0.0857 | 1.0 | 1756 | 0.0635 | 0.9121 | 0.9359 | 0.9238 | 0.9830 |
0.0318 | 2.0 | 3512 | 0.0586 | 0.9245 | 0.9465 | 0.9354 | 0.9857 |
0.0222 | 3.0 | 5268 | 0.0592 | 0.9388 | 0.9522 | 0.9454 | 0.9870 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.6