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: 0.0614
- Precision: 0.9288
- Recall: 0.9374
- F1: 0.9331
- Accuracy: 0.9840
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 |
|---|---|---|---|---|---|---|---|
| 0.2399 | 1.0 | 878 | 0.0694 | 0.9126 | 0.9179 | 0.9152 | 0.9807 |
| 0.0522 | 2.0 | 1756 | 0.0604 | 0.9207 | 0.9342 | 0.9274 | 0.9833 |
| 0.0308 | 3.0 | 2634 | 0.0614 | 0.9288 | 0.9374 | 0.9331 | 0.9840 |
Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
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