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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: 0.1270
- Precision: 0.8236
- Recall: 0.8837
- F1: 0.8526
- Accuracy: 0.9678
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 250 | 0.1647 | 0.7768 | 0.8415 | 0.8079 | 0.9586 |
| 0.2503 | 2.0 | 500 | 0.1340 | 0.8141 | 0.8689 | 0.8406 | 0.9665 |
| 0.2503 | 3.0 | 750 | 0.1270 | 0.8236 | 0.8837 | 0.8526 | 0.9678 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
