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bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1429
- Precision: 0.4954
- Recall: 0.6136
- F1: 0.5482
- Accuracy: 0.9642
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 141 | 0.2894 | 0.4649 | 0.3258 | 0.3831 | 0.9219 |
| No log | 2.0 | 282 | 0.1767 | 0.4706 | 0.4545 | 0.4624 | 0.9487 |
| No log | 3.0 | 423 | 0.1429 | 0.4954 | 0.6136 | 0.5482 | 0.9642 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
