<!-- 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: 0.9336
- Recall: 0.9488
- F1: 0.9412
- Accuracy: 0.9854
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 |
---|---|---|---|---|---|---|---|
0.0897 | 1.0 | 1756 | 0.0648 | 0.9152 | 0.9408 | 0.9278 | 0.9837 |
0.0384 | 2.0 | 3512 | 0.0601 | 0.9277 | 0.9507 | 0.9391 | 0.9859 |
0.0201 | 3.0 | 5268 | 0.0637 | 0.9336 | 0.9488 | 0.9412 | 0.9854 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1