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: 0.0586
- Precision: 0.9293
- Recall: 0.9385
- F1: 0.9339
- Accuracy: 0.9843
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 |
|---|---|---|---|---|---|---|---|
| 0.2436 | 1.0 | 878 | 0.0670 | 0.9190 | 0.9240 | 0.9215 | 0.9815 |
| 0.0505 | 2.0 | 1756 | 0.0591 | 0.9252 | 0.9351 | 0.9301 | 0.9836 |
| 0.0304 | 3.0 | 2634 | 0.0586 | 0.9293 | 0.9385 | 0.9339 | 0.9843 |
Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
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