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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: 0.0595
- Precision: 0.9268
- Recall: 0.9371
- F1: 0.9319
- Accuracy: 0.9842
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 |
---|---|---|---|---|---|---|---|
0.2395 | 1.0 | 878 | 0.0686 | 0.9160 | 0.9235 | 0.9197 | 0.9816 |
0.0547 | 2.0 | 1756 | 0.0593 | 0.9255 | 0.9361 | 0.9308 | 0.9837 |
0.0298 | 3.0 | 2634 | 0.0595 | 0.9268 | 0.9371 | 0.9319 | 0.9842 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2