<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
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: 0.0637
- Precision: 0.9321
- Recall: 0.9492
- F1: 0.9405
- Accuracy: 0.9860
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 |
---|---|---|---|---|---|---|---|
0.0887 | 1.0 | 1756 | 0.0657 | 0.9155 | 0.9322 | 0.9238 | 0.9819 |
0.0334 | 2.0 | 3512 | 0.0653 | 0.9263 | 0.9470 | 0.9365 | 0.9855 |
0.0179 | 3.0 | 5268 | 0.0637 | 0.9321 | 0.9492 | 0.9405 | 0.9860 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3