---
base_model: klue/roberta-small
tags:
- generated_from_trainer
- korean
- klue
widget:
- text: 저는 서울특별시 강남대로에 삽니다. 전화번호는 010-1234-5678이고 주민등록번호는 123456-1234567입니다. 메일주소는 hugging@face.com입니다.
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: klue_roberta_small_ner_identified
  results: []
language:
- ko
pipeline_tag: token-classification
---

<!-- 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_roberta_small_ner_identified

This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0212
- Precision: 0.9803
- Recall: 1.0
- F1: 0.9901
- Accuracy: 0.9980

## Model description

아래 항목에 대한 개체명 인식을 제공합니다.

- 사람이름 [PS] - 낮은 인식률
- 주소 (구 주소 및 도로명 주소) [AD]
- 카드번호 [CN]
- 계좌번호 [BN]
- 운전면허번호 [DN]
- 주민등록번호 [RN]
- 여권번호 [PN]
- 전화번호 [PH]
- 이메일 주소 [EM]

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 15   | 0.2866          | 0.1199    | 0.2739 | 0.1668 | 0.9287   |
| No log        | 2.0   | 30   | 0.1369          | 0.6599    | 0.7996 | 0.7231 | 0.9654   |
| No log        | 3.0   | 45   | 0.0629          | 0.8088    | 0.9042 | 0.8538 | 0.9915   |
| No log        | 4.0   | 60   | 0.0381          | 0.9760    | 0.9978 | 0.9868 | 0.9969   |
| No log        | 5.0   | 75   | 0.0276          | 0.9781    | 0.9955 | 0.9868 | 0.9981   |
| No log        | 6.0   | 90   | 0.0238          | 0.9803    | 1.0    | 0.9901 | 0.9979   |
| No log        | 7.0   | 105  | 0.0224          | 0.9803    | 1.0    | 0.9901 | 0.9979   |
| No log        | 8.0   | 120  | 0.0212          | 0.9803    | 1.0    | 0.9901 | 0.9980   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1

### Use
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("vitus9988/klue_roberta_small_ner_identified")
model = AutoModelForTokenClassification.from_pretrained("vitus9988/klue_roberta_small_ner_identified")

nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
example = """
저는 서울특별시 강남대로 56길 100호에 삽니다. 전화번호는 010-1234-5678이고 주민등록번호는 123456-1234567입니다. 메일주소는 hugging@face.com입니다.
"""

ner_results = nlp(example)
for i in ner_results:
    print(i)

#{'entity_group': 'AD', 'score': 0.79996574, 'word': '서울특별시 강남대로 56길 100호', 'start': 4, 'end': 23}
#{'entity_group': 'PH', 'score': 0.948794, 'word': '010 - 1234 - 5678', 'start': 36, 'end': 49}
#{'entity_group': 'RN', 'score': 0.90686846, 'word': '123456 - 1234567', 'start': 60, 'end': 74}
#{'entity_group': 'EM', 'score': 0.935588, 'word': 'hugging @ face. com', 'start': 85, 'end': 101}

```