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bert-finetuned-ner
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0589
- Overall Precision: 0.9362
- Overall Recall: 0.9500
- Overall F1: 0.9430
- Overall Accuracy: 0.9873
- Loc F1: 0.9616
- Misc F1: 0.8783
- Org F1: 0.9121
- Per F1: 0.9797
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 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Loc F1 | Misc F1 | Org F1 | Per F1 |
---|---|---|---|---|---|---|---|---|---|---|---|
0.0745 | 1.0 | 1756 | 0.0556 | 0.9183 | 0.9345 | 0.9263 | 0.9848 | 0.9501 | 0.8499 | 0.8775 | 0.9765 |
0.0321 | 2.0 | 3512 | 0.0542 | 0.9346 | 0.9475 | 0.9410 | 0.9872 | 0.9618 | 0.8761 | 0.9073 | 0.9773 |
0.0172 | 3.0 | 5268 | 0.0589 | 0.9362 | 0.9500 | 0.9430 | 0.9873 | 0.9616 | 0.8783 | 0.9121 | 0.9797 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1