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: 0.0656
- Precision: 0.9308
- Recall: 0.9482
- F1: 0.9394
- Accuracy: 0.9858
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 |
---|---|---|---|---|---|---|---|
0.0877 | 1.0 | 1756 | 0.0811 | 0.9077 | 0.9273 | 0.9174 | 0.9804 |
0.0341 | 2.0 | 3512 | 0.0642 | 0.9234 | 0.9448 | 0.9340 | 0.9854 |
0.0187 | 3.0 | 5268 | 0.0656 | 0.9308 | 0.9482 | 0.9394 | 0.9858 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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Dataset used to train cindymc/bert-finetuned-ner
Evaluation results
- Precision on conll2003self-reported0.931
- Recall on conll2003self-reported0.948
- F1 on conll2003self-reported0.939
- Accuracy on conll2003self-reported0.986