---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# NER-BERT

This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/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