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: 0.0596
- Precision: 0.9279
- Recall: 0.9463
- F1: 0.9370
- Accuracy: 0.9851
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: 16
- eval_batch_size: 16
- 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 |
---|---|---|---|---|---|---|---|
0.2226 | 1.0 | 878 | 0.0743 | 0.8985 | 0.9278 | 0.9129 | 0.9798 |
0.0451 | 2.0 | 1756 | 0.0577 | 0.9232 | 0.9470 | 0.9350 | 0.9851 |
0.027 | 3.0 | 2634 | 0.0596 | 0.9279 | 0.9463 | 0.9370 | 0.9851 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.1
- Downloads last month
- 4