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distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0622
- Precision: 0.9251
- Recall: 0.9366
- F1: 0.9308
- Accuracy: 0.9838
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 |
---|---|---|---|---|---|---|---|
0.2415 | 1.0 | 878 | 0.0694 | 0.9143 | 0.9253 | 0.9198 | 0.9814 |
0.0541 | 2.0 | 1756 | 0.0631 | 0.9253 | 0.9329 | 0.9291 | 0.9829 |
0.0298 | 3.0 | 2634 | 0.0622 | 0.9251 | 0.9366 | 0.9308 | 0.9838 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3