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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: 0.0562
- Precision: 0.9322
- Recall: 0.9482
- F1: 0.9401
- 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: 16
- eval_batch_size: 16
- 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 |
---|---|---|---|---|---|---|---|
0.2182 | 1.0 | 878 | 0.0709 | 0.9055 | 0.9290 | 0.9171 | 0.9810 |
0.0485 | 2.0 | 1756 | 0.0574 | 0.9270 | 0.9473 | 0.9371 | 0.9858 |
0.0241 | 3.0 | 2634 | 0.0562 | 0.9322 | 0.9482 | 0.9401 | 0.9863 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.11.0
- Datasets 2.13.1
- Tokenizers 0.13.3