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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: 0.3619
- Precision: 0.7737
- Recall: 0.7568
- F1: 0.7651
- Accuracy: 0.8876
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 |
|---|---|---|---|---|---|---|---|
| 0.3965 | 1.0 | 6529 | 0.3917 | 0.7565 | 0.7324 | 0.7442 | 0.8791 |
| 0.361 | 2.0 | 13058 | 0.3706 | 0.7765 | 0.7453 | 0.7606 | 0.8859 |
| 0.3397 | 3.0 | 19587 | 0.3619 | 0.7737 | 0.7568 | 0.7651 | 0.8876 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
