<!-- 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: 0.0650
- Precision: 0.9357
- Recall: 0.9522
- F1: 0.9439
- Accuracy: 0.9864
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 |
---|---|---|---|---|---|---|---|
0.0874 | 1.0 | 1756 | 0.0702 | 0.9245 | 0.9340 | 0.9293 | 0.9822 |
0.0334 | 2.0 | 3512 | 0.0657 | 0.9314 | 0.9487 | 0.9400 | 0.9856 |
0.0179 | 3.0 | 5268 | 0.0650 | 0.9357 | 0.9522 | 0.9439 | 0.9864 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2