bert-ner-conll
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: 0.2386
- Precision: 0.8425
- Recall: 0.8869
- F1: 0.8641
- Accuracy: 0.9745
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 |
|---|---|---|---|---|---|---|---|
| 0.9778 | 1.0 | 625 | 0.3844 | 0.7416 | 0.7930 | 0.7664 | 0.9583 |
| 0.3746 | 2.0 | 1250 | 0.2624 | 0.8341 | 0.8758 | 0.8544 | 0.9726 |
| 0.2760 | 3.0 | 1875 | 0.2386 | 0.8425 | 0.8869 | 0.8641 | 0.9745 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.23.1