bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0648
- Precision: 0.9329
- Recall: 0.9502
- F1: 0.9415
- Accuracy: 0.9856
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: 8
- eval_batch_size: 8
- 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 |
---|---|---|---|---|---|---|---|
0.0732 | 1.0 | 1756 | 0.0685 | 0.9057 | 0.9355 | 0.9204 | 0.9819 |
0.0345 | 2.0 | 3512 | 0.0641 | 0.9349 | 0.9475 | 0.9412 | 0.9857 |
0.0212 | 3.0 | 5268 | 0.0648 | 0.9329 | 0.9502 | 0.9415 | 0.9856 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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