bert-finetuned-ner
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0005
- Precision: 1.0
- Recall: 0.9967
- F1: 0.9983
- Accuracy: 0.9999
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: 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 160 | 0.0010 | 1.0 | 0.9967 | 0.9983 | 0.9999 |
| No log | 2.0 | 320 | 0.0006 | 1.0 | 0.9967 | 0.9983 | 0.9999 |
| No log | 3.0 | 480 | 0.0005 | 1.0 | 0.9967 | 0.9983 | 0.9999 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1