bert-ner-conll2003
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: 0.0612
- Precision: 0.9358
- Recall: 0.9524
- F1: 0.9440
- Accuracy: 0.9866
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 |
|---|---|---|---|---|---|---|---|
| 0.0748 | 1.0 | 1756 | 0.0691 | 0.9015 | 0.9315 | 0.9162 | 0.9815 |
| 0.0343 | 2.0 | 3512 | 0.0689 | 0.9315 | 0.9453 | 0.9384 | 0.9848 |
| 0.0214 | 3.0 | 5268 | 0.0612 | 0.9358 | 0.9524 | 0.9440 | 0.9866 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for Helenn25/bert-ner-conll2003
Base model
google-bert/bert-base-casedDataset used to train Helenn25/bert-ner-conll2003
Evaluation results
- Precision on conll2003validation set self-reported0.936
- Recall on conll2003validation set self-reported0.952
- F1 on conll2003validation set self-reported0.944
- Accuracy on conll2003validation set self-reported0.987