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: 0.0605
- Precision: 0.9370
- Recall: 0.9517
- F1: 0.9443
- 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 |
---|---|---|---|---|---|---|---|
0.0815 | 1.0 | 1756 | 0.0755 | 0.9042 | 0.9312 | 0.9175 | 0.9797 |
0.0422 | 2.0 | 3512 | 0.0567 | 0.9311 | 0.9504 | 0.9406 | 0.9861 |
0.0263 | 3.0 | 5268 | 0.0605 | 0.9370 | 0.9517 | 0.9443 | 0.9866 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- Downloads last month
- 9
Finetuned from
Dataset used to train banw/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.937
- Recall on conll2003validation set self-reported0.952
- F1 on conll2003validation set self-reported0.944
- Accuracy on conll2003validation set self-reported0.987