---
license: cc-by-sa-4.0
base_model: klue/bert-base
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: klue_ner_bert_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: klue
      type: klue
      config: ner
      split: validation
      args: ner
    metrics:
    - name: Precision
      type: precision
      value: 0.883861132284665
    - name: Recall
      type: recall
      value: 0.8966608084358524
    - name: F1
      type: f1
      value: 0.890214963707426
    - name: Accuracy
      type: accuracy
      value: 0.9781297871646948
---

<!-- 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. -->

# klue_ner_bert_model

This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0843
- Precision: 0.8839
- Recall: 0.8967
- F1: 0.8902
- Accuracy: 0.9781

## Model description

KLUE BERT base is a pre-trained BERT Model on Korean Language. The developers of KLUE BERT base developed the model in the context of the development of the [Korean Language Understanding Evaluation (KLUE) Benchmark](https://arxiv.org/pdf/2105.09680.pdf).

## Intended uses & limitations

## How to Get Started With the Model

```python
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("klue/bert-base")
tokenizer = AutoTokenizer.from_pretrained("klue/bert-base")
```

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0638        | 1.0   | 2626 | 0.0807          | 0.8623    | 0.8702 | 0.8662 | 0.9747   |
| 0.0402        | 2.0   | 5252 | 0.0780          | 0.8756    | 0.8896 | 0.8825 | 0.9770   |
| 0.025         | 3.0   | 7878 | 0.0843          | 0.8839    | 0.8967 | 0.8902 | 0.9781   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3