distilbert-ner-finetuned
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: 0.0587
- Precision: 0.9280
- Recall: 0.9392
- F1: 0.9336
- Accuracy: 0.9855
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.078 | 1.0 | 1756 | 0.0607 | 0.9125 | 0.9285 | 0.9204 | 0.9830 |
| 0.0369 | 2.0 | 3512 | 0.0557 | 0.9233 | 0.9406 | 0.9319 | 0.9853 |
| 0.0248 | 3.0 | 5268 | 0.0587 | 0.9280 | 0.9392 | 0.9336 | 0.9855 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0