distilledbert-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: 0.0597
- Precision: 0.9297
- Recall: 0.9416
- F1: 0.9356
- Accuracy: 0.9857
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 |
|---|---|---|---|---|---|---|---|
| 0.0776 | 1.0 | 1756 | 0.0639 | 0.9077 | 0.9263 | 0.9169 | 0.9823 |
| 0.0368 | 2.0 | 3512 | 0.0583 | 0.9274 | 0.9398 | 0.9335 | 0.9856 |
| 0.0235 | 3.0 | 5268 | 0.0597 | 0.9297 | 0.9416 | 0.9356 | 0.9857 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0