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: 0.0624
- Precision: 0.9389
- Recall: 0.9524
- F1: 0.9456
- Accuracy: 0.9869
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 |
|---|---|---|---|---|---|---|---|
| 0.0773 | 1.0 | 1756 | 0.0671 | 0.9069 | 0.9313 | 0.9190 | 0.9814 |
| 0.0368 | 2.0 | 3512 | 0.0659 | 0.9291 | 0.9455 | 0.9372 | 0.9856 |
| 0.0236 | 3.0 | 5268 | 0.0624 | 0.9389 | 0.9524 | 0.9456 | 0.9869 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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Model tree for mrcolley/bert-finetuned-ner
Base model
google-bert/bert-base-casedDataset used to train mrcolley/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.939
- Recall on conll2003validation set self-reported0.952
- F1 on conll2003validation set self-reported0.946
- Accuracy on conll2003validation set self-reported0.987