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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: 0.0571
- Precision: 0.9235
- Recall: 0.9461
- F1: 0.9347
- Accuracy: 0.9861
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: 32
- 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 | 439 | 0.0652 | 0.9108 | 0.9352 | 0.9229 | 0.9821 |
| 0.0685 | 2.0 | 878 | 0.0567 | 0.9249 | 0.9455 | 0.9351 | 0.9857 |
| 0.034 | 3.0 | 1317 | 0.0571 | 0.9235 | 0.9461 | 0.9347 | 0.9861 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
