practice-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: 0.0664
- Precision: 0.9326
- Recall: 0.9507
- F1: 0.9416
- Accuracy: 0.9872
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 |
---|---|---|---|---|---|---|---|
0.024 | 1.0 | 1756 | 0.0618 | 0.9232 | 0.9468 | 0.9349 | 0.9851 |
0.0212 | 2.0 | 3512 | 0.0647 | 0.9344 | 0.9492 | 0.9417 | 0.9870 |
0.0103 | 3.0 | 5268 | 0.0664 | 0.9326 | 0.9507 | 0.9416 | 0.9872 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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