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: 0.0661
- Precision: 0.9318
- Recall: 0.9495
- F1: 0.9406
- 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 |
---|---|---|---|---|---|---|---|
0.0885 | 1.0 | 1756 | 0.0664 | 0.9189 | 0.9327 | 0.9257 | 0.9820 |
0.0346 | 2.0 | 3512 | 0.0650 | 0.9260 | 0.9456 | 0.9357 | 0.9856 |
0.017 | 3.0 | 5268 | 0.0661 | 0.9318 | 0.9495 | 0.9406 | 0.9854 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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Dataset used to train thucdangvan020999/bert-finetuned-ner
Evaluation results
- Precision on conll2003self-reported0.932
- Recall on conll2003self-reported0.950
- F1 on conll2003self-reported0.941
- Accuracy on conll2003self-reported0.985