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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: 0.0589
- Precision: 0.9250
- Recall: 0.9374
- F1: 0.9312
- Accuracy: 0.9839
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 |
---|---|---|---|---|---|---|---|
0.2343 | 1.0 | 878 | 0.0674 | 0.9177 | 0.9233 | 0.9205 | 0.9818 |
0.0525 | 2.0 | 1756 | 0.0582 | 0.9245 | 0.9362 | 0.9304 | 0.9837 |
0.0288 | 3.0 | 2634 | 0.0589 | 0.9250 | 0.9374 | 0.9312 | 0.9839 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1