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: 0.0809
- Precision: 0.8865
- Recall: 0.9185
- F1: 0.9022
- Accuracy: 0.9802
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_FUSED 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 |
|---|---|---|---|---|---|---|---|
| 0.0772 | 1.0 | 625 | 0.0849 | 0.8781 | 0.9143 | 0.8959 | 0.9793 |
| 0.0568 | 2.0 | 1250 | 0.0818 | 0.8835 | 0.9177 | 0.9003 | 0.9794 |
| 0.0494 | 3.0 | 1875 | 0.0809 | 0.8865 | 0.9185 | 0.9022 | 0.9802 |
Framework versions
- Transformers 5.18.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.23.2