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: 0.0636
- Precision: 0.9342
- Recall: 0.9492
- F1: 0.9416
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0759 | 1.0 | 1756 | 0.0645 | 0.9062 | 0.9337 | 0.9198 | 0.9813 |
| 0.0371 | 2.0 | 3512 | 0.0652 | 0.9327 | 0.9473 | 0.9400 | 0.9857 |
| 0.0229 | 3.0 | 5268 | 0.0636 | 0.9342 | 0.9492 | 0.9416 | 0.9863 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for LucasMagnana/bert-finetuned-ner
Base model
google-bert/bert-base-casedDataset used to train LucasMagnana/bert-finetuned-ner
Evaluation results
- Precision on conll2003self-reported0.934
- Recall on conll2003self-reported0.949
- F1 on conll2003self-reported0.942
- Accuracy on conll2003self-reported0.986