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: 0.0602
- Precision: 0.9360
- Recall: 0.9505
- F1: 0.9432
- Accuracy: 0.9867
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 |
---|---|---|---|---|---|---|---|
0.0744 | 1.0 | 1756 | 0.0634 | 0.9098 | 0.9354 | 0.9224 | 0.9828 |
0.0334 | 2.0 | 3512 | 0.0631 | 0.9345 | 0.9463 | 0.9404 | 0.9854 |
0.0208 | 3.0 | 5268 | 0.0602 | 0.9360 | 0.9505 | 0.9432 | 0.9867 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
- Downloads last month
- 6
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
Model tree for Jackson107/bert-finetuned-ner
Base model
google-bert/bert-base-casedDataset used to train Jackson107/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.936
- Recall on conll2003validation set self-reported0.951
- F1 on conll2003validation set self-reported0.943
- Accuracy on conll2003validation set self-reported0.987