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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: 0.0623
- Precision: 0.9242
- Recall: 0.9349
- F1: 0.9295
- Accuracy: 0.9834
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 |
---|---|---|---|---|---|---|---|
0.2385 | 1.0 | 878 | 0.0708 | 0.9140 | 0.9216 | 0.9178 | 0.9808 |
0.055 | 2.0 | 1756 | 0.0626 | 0.9209 | 0.9340 | 0.9274 | 0.9828 |
0.0309 | 3.0 | 2634 | 0.0623 | 0.9242 | 0.9349 | 0.9295 | 0.9834 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2