bert-finetuned-ner
This model is a fine-tuned version of BAAI/bge-small-en-v1.5 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0814
- Precision: 0.8811
- Recall: 0.9189
- F1: 0.8996
- Accuracy: 0.9801
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0941 | 1.0 | 626 | 0.0899 | 0.8697 | 0.9106 | 0.8897 | 0.9791 |
| 0.0657 | 2.0 | 1252 | 0.0834 | 0.8775 | 0.9123 | 0.8946 | 0.9798 |
| 0.0537 | 3.0 | 1878 | 0.0814 | 0.8811 | 0.9189 | 0.8996 | 0.9801 |
Framework versions
- Transformers 4.50.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.21.4