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: 0.0603
- Precision: 0.9317
- Recall: 0.9510
- F1: 0.9413
- Accuracy: 0.9866
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 |
|---|---|---|---|---|---|---|---|
| 0.0872 | 1.0 | 1756 | 0.0660 | 0.9152 | 0.9350 | 0.9250 | 0.9827 |
| 0.0386 | 2.0 | 3512 | 0.0579 | 0.9374 | 0.9498 | 0.9436 | 0.9864 |
| 0.0225 | 3.0 | 5268 | 0.0603 | 0.9317 | 0.9510 | 0.9413 | 0.9866 |
Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
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Dataset used to train Emmanuel/bert-finetuned-ner
Evaluation results
- Precision on conll2003self-reported0.932
- Recall on conll2003self-reported0.951
- F1 on conll2003self-reported0.941
- Accuracy on conll2003self-reported0.987