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: 0.0663
- Precision: 0.9295
- Recall: 0.9473
- F1: 0.9383
- 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 |
---|---|---|---|---|---|---|---|
0.0743 | 1.0 | 1756 | 0.0704 | 0.9102 | 0.9360 | 0.9229 | 0.9814 |
0.0355 | 2.0 | 3512 | 0.0677 | 0.9289 | 0.9455 | 0.9371 | 0.9851 |
0.0248 | 3.0 | 5268 | 0.0663 | 0.9295 | 0.9473 | 0.9383 | 0.9854 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Finetuned from
Dataset used to train hfjbkim/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.929
- Recall on conll2003validation set self-reported0.947
- F1 on conll2003validation set self-reported0.938
- Accuracy on conll2003validation set self-reported0.985