---
base_model: klue/roberta-base
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: klue_ner_roberta_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: 0.9545986426398315
    - name: Recall
      type: recall
      value: 0.9557169634489222
    - name: F1
      type: f1
      value: 0.955157475705421
    - name: Accuracy
      type: accuracy
      value: 0.9883703228112445
---

<!-- 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_roberta_model

This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0487
- Precision: 0.9546
- Recall: 0.9557
- F1: 0.9552
- Accuracy: 0.9884

## Model description

Pretrained RoBERTa Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details.

## Intended uses & limitations

## How to use

_NOTE:_ Use `BertTokenizer` instead of RobertaTokenizer. (`AutoTokenizer` will load `BertTokenizer`)

```python
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("klue/roberta-base")
tokenizer = AutoTokenizer.from_pretrained("klue/roberta-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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0449        | 1.0   | 2626 | 0.0601          | 0.9361    | 0.9176 | 0.9267 | 0.9830   |
| 0.0262        | 2.0   | 5252 | 0.0469          | 0.9484    | 0.9510 | 0.9497 | 0.9874   |
| 0.0144        | 3.0   | 7878 | 0.0487          | 0.9546    | 0.9557 | 0.9552 | 0.9884   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3