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: 0.0582
- Precision: 0.9371
- Recall: 0.9500
- F1: 0.9435
- 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: 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 |
---|---|---|---|---|---|---|---|
0.0798 | 1.0 | 1756 | 0.0747 | 0.9068 | 0.9320 | 0.9192 | 0.9800 |
0.0422 | 2.0 | 3512 | 0.0546 | 0.9314 | 0.9482 | 0.9397 | 0.9857 |
0.0256 | 3.0 | 5268 | 0.0582 | 0.9371 | 0.9500 | 0.9435 | 0.9863 |
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 tintinjian12999/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.937
- Recall on conll2003validation set self-reported0.950
- F1 on conll2003validation set self-reported0.944
- Accuracy on conll2003validation set self-reported0.986