metadata
library_name: transformers
license: mit
base_model: dslim/bert-base-NER
tags:
- ner
- bert
- token-classification
- generated_from_trainer
model-index:
- name: NER-BERT
results: []
NER-BERT
This model is a fine-tuned version of dslim/bert-base-NER on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- Token Accuracy: 1.0000
- Token Precision: 1.0000
- Token Recall: 1.0000
- Token F1: 1.0000
- Entity Precision: 0.9999
- Entity Recall: 0.9999
- Entity F1: 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: 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Token Accuracy | Token Precision | Token Recall | Token F1 | Entity Precision | Entity Recall | Entity F1 |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0004 | 1.0 | 2250 | 0.0002 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9995 | 0.9996 | 0.9996 |
| 0.0003 | 2.0 | 4500 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9998 | 0.9999 | 0.9998 |
| 0.0001 | 3.0 | 6750 | 0.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9999 | 0.9999 | 0.9999 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1