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nerTesting
This model is a fine-tuned version of dslim/bert-large-NER on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 31.3186
- Precision: 0.0222
- Recall: 0.0274
- F1: 0.0246
- Accuracy: 0.7634
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: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
75.0868 | 1.0 | 878 | 48.4896 | 0.0526 | 0.0663 | 0.0586 | 0.7646 |
42.534 | 2.0 | 1756 | 37.5725 | 0.0220 | 0.0271 | 0.0243 | 0.7620 |
32.9417 | 3.0 | 2634 | 33.3310 | 0.0224 | 0.0269 | 0.0244 | 0.7652 |
28.2469 | 4.0 | 3512 | 31.3186 | 0.0222 | 0.0274 | 0.0246 | 0.7634 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2