<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
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: 0.0645
- Precision: 0.9356
- Recall: 0.9444
- F1: 0.9400
- Accuracy: 0.9851
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: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.2464 | 1.0 | 878 | 0.0679 | 0.9223 | 0.9248 | 0.9236 | 0.9818 |
0.0526 | 2.0 | 1756 | 0.0574 | 0.9290 | 0.9367 | 0.9328 | 0.9837 |
0.0274 | 3.0 | 2634 | 0.0594 | 0.9282 | 0.9400 | 0.9341 | 0.9843 |
0.0173 | 4.0 | 3512 | 0.0617 | 0.9349 | 0.9428 | 0.9388 | 0.9851 |
0.0119 | 5.0 | 4390 | 0.0645 | 0.9356 | 0.9444 | 0.9400 | 0.9851 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cpu
- Datasets 2.8.0
- Tokenizers 0.13.2