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: 0.0626
- Precision: 0.9338
- Recall: 0.9498
- F1: 0.9418
- Accuracy: 0.9864
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 |
---|---|---|---|---|---|---|---|
0.0751 | 1.0 | 1756 | 0.0595 | 0.9048 | 0.9345 | 0.9194 | 0.9832 |
0.0338 | 2.0 | 3512 | 0.0645 | 0.9311 | 0.9467 | 0.9388 | 0.9859 |
0.0222 | 3.0 | 5268 | 0.0626 | 0.9338 | 0.9498 | 0.9418 | 0.9864 |
Framework versions
- Transformers 4.36.1
- Pytorch 2.0.1+cu117
- Datasets 2.16.0
- Tokenizers 0.15.0
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