bert-ner-finetuned
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: 0.0655
- Precision: 0.9378
- Recall: 0.9515
- F1: 0.9446
- Accuracy: 0.9863
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0747 | 1.0 | 1756 | 0.0672 | 0.9094 | 0.9354 | 0.9222 | 0.9819 |
| 0.0329 | 2.0 | 3512 | 0.0724 | 0.9358 | 0.9472 | 0.9415 | 0.9846 |
| 0.0212 | 3.0 | 5268 | 0.0655 | 0.9378 | 0.9515 | 0.9446 | 0.9863 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0