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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: 0.0591
- Precision: 0.9047
- Recall: 0.9362
- F1: 0.9202
- Accuracy: 0.9836
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: 64
- eval_batch_size: 64
- 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 | 220 | 0.0930 | 0.8595 | 0.8995 | 0.8790 | 0.9737 |
No log | 2.0 | 440 | 0.0629 | 0.8931 | 0.9322 | 0.9122 | 0.9819 |
0.1689 | 3.0 | 660 | 0.0591 | 0.9047 | 0.9362 | 0.9202 | 0.9836 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3